ASPIRE-VINS: Adaptive Spline-based Visual-inertial Navigation System With Robust 3D Measurement Residuals

Eungchang Mason Lee; Hyun Myung; KwangYik Jung; Taekjun Oh

REVIEW 2 major objections 4 minor 32 references

ASPIRE-VINS: Adaptive Spline-based Visual-inertial Navigation System With Robust 3D Measurement Residuals

T0 review · 2 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper claims ASPIRE-VINS, coupling adaptive knot placement, multi-resolution splines, and 3D ray-consistency residuals, achieves the lowest mean ATE RMSE of 0.212 m across nine VIO benchmark sequences, a 5.36 percent improvement over…

desk verdict Plausible continuous-time VINS extension with a real partition-of-unity gap in the MRS basis; worth reviewing, but the central representation needs a proof or fix before the results can be trusted. read the letter →

arxiv 2608.12840 v1 pith:KPWMASPD submitted 2026-08-13 cs.RO

Kwangyik Jung , Eungchang Mason Lee , Taekjun Oh , Hyun Myung This is my paper · ORCID

classification cs.RO

keywords adaptiveknotplacementmulti-resolutionB-splinecontinuous-timetrajectoryvisual-inertialodometry3Dmeasurement-spaceresidualLiegroupoptimizationsplineinterpolationaccuracy

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that a continuous-time visual-inertial odometry system can gain both accuracy and robustness by making its trajectory representation adapt to motion and by checking visual constraints in 3D measurement space rather than in the image plane. It proposes ASPIRE-VINS, which combines adaptive knot placement (AKP), multi-resolution splines (MRS), and 3D measurement-space residuals (3D-MSR) in one Lie-group optimization. The reported experiments show the lowest mean ATE RMSE of 0.212 m on nine VIO benchmark sequences, a 5.36 percent improvement over the best continuous-time baseline, and the lowest RMSE on all four selected Hilti-Oxford sequences. A controlled ablation with identical feature tracks attributes part of the gain to temporal adaptivity and part to 3D-MSR, so the reader cares because the paper offers a concrete route past two known limits of continuous-time VINS: uniform knots and pixel-plane reprojection.

What carries the argument

The load-bearing object is the effective interpolation weight $w^\mu_h(t)$ in Eq. (7), which grafts lower-order B-spline refinement terms onto a fixed non-uniform B-spline basis with bounded coefficients $\gamma^\mu_h$. AKP fixes where knots go: each interval $\Delta t_r$ is clipped to $[\Delta t_{\min}, \Delta t_{\max}]$ and inversely scaled by local velocity change, so fast segments get denser temporal support. 3D-MSR is the other half of the mechanism: the residual $(I - V)(R^m_r P_r + t^m_r)$ projects the transformed reference feature onto the plane orthogonal to the calibrated ray, enforcing bearing consistency without constraining depth. These pieces are optimized jointly in $SE(3)$ through Lie-group retraction, with inertial residuals and a tangent-space smoothness prior regularizing the trajectory.

What would settle it

Evaluate the sum $\sum_\mu w^\mu_h(t)$ from Eq. (7) on the actual AKP-generated non-uniform knot sequences used in the experiments, across the full time span. If that sum is not constant in $t$, then setting every control point to the same pose would still produce a moving trajectory, and the reported ATE numbers cannot be trusted; if the sum is constant, the central claim survives this test.

Watch

Extended reading notes

Core claim

The central discovery, stated on the paper's own terms, is that a single continuous-time trajectory can carry non-uniform, motion-dependent temporal resolution and still deliver precise inertial and visual residuals. Concretely, ASPIRE-VINS places spline knots via AKP from a locally filtered velocity-change statistic $\rho_r = \|\tilde{v}_{i_{r+1}} - \tilde{v}_{i_r}\|$, refines the spline locally through MRS with bounded activation coefficients $\gamma^\mu_h$, and replaces image-plane reprojection with the 3D-MSR residual $f_P = (I - V)(R^m_r P_r + t^m_r)$, which cancels the component along the calibrated observation ray. Optimizing this combined loss produces the lowest mean ATE RMSE of $0.212$ m on the nine-sequence benchmark and the lowest RMSE on all four selected Hilti-Oxford sequences; the controlled ablation shows that the adaptive representation and 3D-MSR each contribute under identical feature tracks.

Load-bearing premise

The effective interpolation weights in Eq. (7) are assumed to form a valid spline basis: they must sum to a constant over the non-uniform knot vector AKP produces, but the paper never proves this, and if the sum varies with time, even an all-equal control-point trajectory would show spurious motion, invalidating every reported error.

Editorial extensions

If this is right

On the VIO benchmark, adaptive non-uniform knots and multi-resolution refinement lower trajectory error most in fast translation and head-turning sequences, where fixed-resolution continuous-time baselines lose accuracy.
The controlled ablation implies that 3D-MSR alone, without changing the temporal model, improves both discrete-time and continuous-time backends under identical feature tracks, so bearing-consistency residuals can be dropped into other estimators.
AKP coarsens knot density in low-dynamic intervals, so the representation avoids redundant parameters where motion is smooth, while retaining enough resolution for sharp maneuvers.
With fixed parameters across benchmark, handheld, and Hilti-Oxford data, the method's accuracy advantage persists under visual degradation, suggesting the adaptive backend is not overfit to one dataset.
Runtime of about 56 ms per frame (about 18 FPS) on the tested CPU means adaptive continuous-time optimization remains practical, though slower than square-root filtering baselines.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the partition-of-unity assumption for $w^\mu_h(t)$ holds only approximately on AKP's non-uniform knot vectors, the same construction could be made rigorous by renormalizing the weights or by deriving an exact basis; doing so would put the reported numbers on firmer ground without changing the experimental comparison.
The 3D-MSR idea is sensor-agnostic: the same ray-orthogonal residual could be applied in LiDAR-inertial or UWB-inertial continuous-time estimators, where range or bearing measurements arrive at asynchronous timestamps.
The AKP velocity-change statistic is a simple proxy for motion complexity; replacing it with a learned or spectral measure of local dynamics could extend adaptivity to scenes with periodic or high-frequency vibration.
The paper's own closing remark about Chebyshev-based barycentric rational interpolation suggests the authors expect the current B-spline basis to be the limiting factor for stability; an independent reader could test whether substituting a rational interpolant removes knot sensitivity without changing the reported accuracy hierarchy.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Colleague,

The thing to know: this paper is a plausible engineering contribution with a real, load-bearing math gap in its core trajectory representation. The specific combination — adaptive knot placement from CT-UIO, a multi-resolution spline hierarchy, and a 3D ray-orthogonal visual residual all inside a continuous-time VINS backend — is new in the cited literature. The controlled ablation with identical feature tracks is genuinely good practice: it isolates the backend changes without front-end confounding. The fixed hyperparameters and explicit acknowledgment that each sequence was run once are honest.

The soft spot is not a tuning detail. Equation (7) defines the MRS effective weights as B-spline basis functions plus gamma-scaled correction terms. For a standard B-spline basis, the partition-of-unity property follows from the recurrence. The correction terms cancel under uniform knot spacing, but under the non-uniform knot vector generated by AKP they do not. The paper never proves the weights sum to one. If they do not, a set of identical control points yields a time-varying pose, which means the representation is not actually a valid spline. That would undermine the interpretation of every reported ATE number, not just the ablation. The stress-test note is on target here.

Second soft spot: the AKP velocity statistic is described for linear velocity, but pure-rotation sequences are used to demonstrate the adaptive mechanism. It is not clear how rotational motion enters the knot allocation. That needs clarification, even if the answer is that head-turning cases include translation.

Third: the empirical evidence is single-run, with no error bars. The benchmark gains are modest (5.36% over OKVIS-CT), and the larger Hilti-Oxford gains would be more convincing with repeated trials and ideally code release. This is a standard concern for systems papers, not disqualifying on its own.

What is solid: the loss formulation and residual equations are internally consistent; the ablation design is above average; the limitations section acknowledges knot-init sensitivity and depth non-observability. I do not see a circular step in the evaluation.

Who should read it: people building continuous-time VINS backends. It deserves a serious referee. My recommendation: send it out, but require a proof or numerical verification that the MRS weights form a partition of unity on non-uniform knots, plus a normalization step if needed, and repeated-run or code-backed evidence before accepting.

That's my take.

Referee Report

2 major / 4 minor

Summary. The paper proposes ASPIRE-VINS, a continuous-time visual-inertial navigation system that combines adaptive knot placement (AKP), multi-resolution splines (MRS), and 3D measurement-space residuals (3D-MSR). The trajectory is represented as a weighted sum of local SE(3) coefficients via Eq. (6), with effective interpolation weights modified by motion-driven refinement terms in Eq. (7). AKP allocates knots according to local motion statistics, MRS adds bounded local refinement across resolution levels, and 3D-MSR replaces 2D reprojection with a ray-orthogonal bearing residual. The authors report end-to-end comparisons on nine VIO benchmark sequences, two custom handheld sequences, and four Hilti-Oxford sequences, claiming the lowest mean ATE RMSE of 0.212 m on the VIO benchmark (5.36% improvement over OKVIS-CT) and the lowest RMSE on all selected Hilti-Oxford sequences. A controlled ablation with identical feature tracks is used to isolate the contributions of the temporal backend and the residual formulation.

Significance. If the theoretical foundation holds, the paper makes a useful contribution to continuous-time VINS by addressing the rigidity of uniform knot spacing and proposing a residual that operates in 3D measurement space. The controlled-ablation design, which keeps feature tracks, triangulated landmarks, and outlier rejection identical across variants, is a clear strength and allows a cleaner attribution of accuracy gains to the backend components than typical end-to-end comparisons. The explicit formulas for knot placement, refinement activation, and residuals are reproducible in principle, and the fixed-parameter protocol across datasets is commendable. However, the central trajectory model hinges on an unproven and likely false partition-of-unity property of the modified spline weights, which undermines the claim that Eq. (6) is a valid spline representation and could confound the reported accuracy gains.

major comments (2)

[Section III-D, Eq. (7) and Eq. (6)] The effective interpolation weights w^h_μ(t) in Eq. (7) are not shown to satisfy the partition-of-unity property. For standard B-splines, Σ_μ B_{μ,k}(t) = 1 follows from the recurrence, but the added γ terms with denominators t_{μ+1}-t_μ and t_{μ+1}-t_μ do not cancel under the standard recurrence and produce a time-dependent sum for non-uniform knots. Consequently, if all control points at a resolution level are set to a fixed nonzero tangent vector c, the argument Σ_μ w^h_μ(t) c in Eq. (6) varies with t, so the retracted trajectory is not constant. This violates the fundamental property expected of a spline basis and introduces a data-independent time-varying bias that could inflate the reported gains in Tables II, III, and V. The authors should either prove that Σ_μ w^h_μ(t) is constant over the AKP knot vector, add an explicit normalization step and re-derive the residual and smoothness-prior Jacobians accordingly, or provide a rigorous argument that the time-varying sum does not affect the optimization results in the tested scenarios.
[Section IV-B, Tables II and V] The experimental evaluation reports only a single run per sequence and explicitly states that the metrics are trajectory-level statistics rather than mean and standard deviation over repeated runs. The claimed advantage over OKVIS-CT on the VIO benchmark is small (mean RMSE 0.212 m vs. 0.224 m, about 5.4%), and on several sequences (square-normal, circle-normal, infinity-normal) OKVIS-CT or Ctrl-VIO matches or beats ASPIRE-VINS. Without repeated runs, cross-validation, or a statistical test (e.g., a paired test over trajectory segments), the superiority claim for the mean is not robust. The Hilti-Oxford improvements are larger, but still need uncertainty quantification to rule out tuning or incidental effects of the frontend. At minimum, the authors should report per-sequence error distributions, perform a segment-wise analysis, or run each method multiple times with noise-injected inputs.

minor comments (4)

[Section III-B, Eq. (3)] The covariance matrices Σ_ω, Σ_a, Σ_P, Σ_˙ω, and Σ_¨p are not specified; the authors should state whether they are diagonal, how they are initialized, and whether they are tuned per dataset or fixed.
[Section III-C, Eq. (4)] The residual f_P in Eq. (4) is a 3-vector that lies in the 2D subspace orthogonal to the ray direction r, so the associated information matrix is rank-deficient. The paper should explain how the covariance Σ_P handles this singularity (e.g., through a pseudo-inverse or a 2D parametrization of the tangent plane).
[Table I] The AKP gain α is stated as 'normalized' but the actual normalization rule is not given. Please specify how α is computed or provide the value used in the experiments.
[Introduction / Section III] The acronym ASPIRE is defined as 'Adaptive Spline-based Platform for Inertial–visual Robust Estimation' in Section III but the title and abstract use 'Adaptive Spline-based Visual-inertial Navigation System'. Please make the expansion consistent.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the components are defined from sensor statistics and evaluated against external benchmarks, with only a non-load-bearing self-citation for a standard retraction that is restated in the paper.

full rationale

ASPIRE-VINS does not fit any parameter to the trajectory errors it later reports. The adaptive knot timestamps (Eq. 10) and MRS activation weights (Eqs. 8-9) are deterministic functions of the IMU velocity-change statistics, not of the estimated poses or ATE values, and the hyperparameters (alpha, lambda, gamma bounds, knot bounds) are fixed across all sequences. The 3D-MSR residual (Eqs. 4-5) is a standard ray-orthogonal bearing-consistency term whose reference landmarks are optimization variables, so it does not presuppose the trajectory being evaluated. The reported comparisons are against external datasets (VIO benchmark, Hilti-Oxford) and published baselines with fixed parameters, and the controlled ablation shares identical feature tracks across variants, which isolates the backend components rather than fitting them to the final errors. The only self-citation of the authors' MSCKF-DVIO [26] is for the standard Lie-group retraction x plus delta xi equals exp(delta xi) times x, and that formula is restated in Eq. (2), so the citation is not load-bearing. The theoretical concern about a possible partition-of-unity failure in Eq. (7) is a mathematical-validity and correctness risk, not an input-output circularity, because the effective weights are not fitted to the trajectory outputs. Thus no circular step is present.

Assumptions & free parameters 6 free parameters · 6 assumptions · 0 invented entities

The central claim depends on several hand-chosen hyperparameters and on the unproven partition-of-unity property of the MRS weights. No new physical entities are introduced. The AKP and MRS statistics are computed from the same sensor data used for estimation, but they are not fitted to the final trajectory errors.

free parameters (6)

AKP gain alpha = normalized (no numeric value given)
Maps local velocity-change magnitude to knot spacing in Eq. (10); a single normalization rule is used across sequences but the rule is not specified quantitatively.
Smoothness weight lambda = 0.05
Sets the strength of tangent-space motion regularization in Eq. (3); chosen by hand.
Knot bounds dt_min, dt_max = 0.03 s, 0.25 s
Clip adaptive knot spacing in Eq. (10); selected from sensor rates and expected motion bandwidth, not per-sequence.
MRS activation bounds gamma_min, gamma_max = 0.0, 1.0
Clip refinement activation in Eq. (9); chosen by hand.
Smoothing half-window R = 2 IMU samples
Defines the locally filtered velocity-change statistic in Eq. (8); chosen by hand.
Residual covariance matrices = not specified
Sigma_omega, Sigma_a, Sigma_P, Sigma_dotomega, Sigma_ddotp in Eq. (3) are weights that affect the result but their values are not disclosed.

assumptions (6)

standard math Standard B-spline basis functions provide an affine-invariant interpolation for a given knot vector.
Used implicitly in Section III-D when defining the weighted sum of local pose coefficients.
domain assumption The locally filtered linear velocity-change magnitude rho_r is a sufficient proxy for the temporal resolution needed by the trajectory.
Underpins AKP in Eq. (10) and MRS activation in Eq. (8); if rotation produces no linear velocity change, the adaptive mechanism may fail on pure-rotation motion.
domain assumption IMU and camera are hardware-time synchronized and intrinsically calibrated.
Stated in Section III-C: the 3D-MSR assumes calibrated observations and does not compensate for calibration errors.
ad hoc to paper The effective MRS interpolation weights in Eq. (7) satisfy partition of unity under non-uniform knots.
Load-bearing but unproven; if the weights do not sum to a constant, constant control points would produce time-varying motion.
domain assumption Reference landmark depths from prior multi-view triangulation and bundle adjustment are accurate enough that the unconstrained along-ray component does not bias the result.
Stated in Section III-C: 3D-MSR does not make depth observable under pure rotation or insufficient parallax.
standard math The exponential map and retraction on SE(3) behave as a local diffeomorphism in the optimization domain.
Used in Eqs. (2) and (6) for state updates and spline retraction; standard for Lie group optimization.

how reviews work

0 comments

Cite this review

Pith. "Pith review of ASPIRE-VINS: Adaptive Spline-based Visual-inertial Navigation System With Robust 3D Measurement Residuals." pith.science (2026). https://pith.science/paper/KPWMASPD

@misc{pith2026260812840,
  author       = {Pith},
  title        = {Pith review of: ASPIRE-VINS: Adaptive Spline-based Visual-inertial Navigation System With Robust 3D Measurement Residuals},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KPWMASPD}},
  note         = {Machine review of arXiv:2608.12840}
}

read the original abstract

Visual-inertial navigation systems estimate six-degree-of-freedom motion by fusing visual and inertial data. Modern discrete-time methods with IMU preintegration provide strong accuracy and efficiency, but keyframe-based representations can be less flexible when residuals must be evaluated at arbitrary timestamps or when motion-dependent temporal resolution is needed. Continuous-time splines address this issue by representing the trajectory as a smooth temporal function, but uniformly spaced knots can under-represent rapid dynamics or over-parameterize static intervals. This letter proposes ASPIRE-VINS, a continuous-time VINS framework that combines adaptive knot placement (AKP), multi-resolution splines (MRS), and 3D measurement-space residuals (3D-MSR). AKP allocates knots according to local motion variation, MRS adds bounded local refinement in tangent space, and 3D-MSR provides bearing consistency by aligning transformed features with calibrated observation rays in 3D measurement space. Experiments show that ASPIRE-VINS achieves competitive or lower trajectory errors than the compared baselines, demonstrating the effectiveness of motion-adaptive continuous-time trajectory modeling under diverse motion and sensing conditions.

Figures

Figures reproduced from arXiv: 2608.12840 by the authors.

**Figure 2.** Conceptual illustration of 3D-MSR. The reference 3D feature [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Hardware configuration for custom handheld data collection. [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: Evaluation on custom handheld datasets with PALoc ground truth. ( [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]

**Figure 5.** Figure 5: Evaluation on the Hilti-Oxford exp18 sequence. (a) shows the estimated trajectories projected onto the XY plane, highlighting long-term drift behavior under repetitive motion. (b) illustrates a narrow spiral staircase with highly repetitive geometry, which induces stro…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

32 extracted references · 26 canonical work pages

[1]

Robust stereo visual-inertial odometry for fast autonomous flight,

K. Sun, K. Mohta, B. Pfrommer, M. Watterson, S. Liu, Y . Mulgaonkar, C. J. Taylor, and V . Kumar, “Robust stereo visual-inertial odometry for fast autonomous flight,”IEEE Robot. Automat. Lett., vol. 3, no. 2, pp. 965–972, 2018

work page 2018
[2]

VINS-Mono: A robust and versatile monocular visual-inertial state estimator,

T. Qin, P. Li, and S. Shen, “VINS-Mono: A robust and versatile monocular visual-inertial state estimator,”IEEE Trans. Robot., vol. 34, no. 4, pp. 1004–1020, 2018

2018
[3]

PL-VINS: Real-time monocular visual-inertial SLAM with point and line,

Q. Fu, J. Wang, H. Yu, I. Ali, F. Guo, and H. Zhang, “PL-VINS: Real-time monocular visual-inertial SLAM with point and line,”arXiv preprint arXiv:2009.07462, 2020

arXiv 2009
[4]

√ vins: Robust and ultra- fast square-root filter-based 3D motion tracking,

Y . Peng, C. Chen, K. Wu, and G. Huang, “ √ vins: Robust and ultra- fast square-root filter-based 3D motion tracking,”IEEE Trans. Robot., vol. 41, pp. 6570–6589, 2025

work page 2025
[5]

Continuous-time batch estimation using temporal basis functions,

P. Furgale, T. D. Barfoot, and G. Sibley, “Continuous-time batch estimation using temporal basis functions,” inProc. IEEE Int. Conf. Robot. Automat., 2012, pp. 2088–2095

work page 2012
[6]

Continuous- discrete extended Kalman filter on matrix Lie groups using concentrated Gaussian distributions,

G. Bourmaud, R. M ´egret, M. Arnaudon, and A. Giremus, “Continuous- discrete extended Kalman filter on matrix Lie groups using concentrated Gaussian distributions,”J. Math. Imaging Vis., vol. 51, no. 1, pp. 209– 228, 2015

work page 2015
[7]

Continuous- time visual-inertial odometry for event cameras,

E. Mueggler, G. Gallego, H. Rebecq, and D. Scaramuzza, “Continuous- time visual-inertial odometry for event cameras,”IEEE Trans. Robot., vol. 34, no. 6, pp. 1425–1440, 2018

work page 2018
[8]

Ctrl-VIO: Continuous-time visual-inertial odometry for rolling shutter cameras,

X. Lang, J. Lv, J. Huang, Y . Ma, Y . Liu, and X. Zuo, “Ctrl-VIO: Continuous-time visual-inertial odometry for rolling shutter cameras,” IEEE Robot. Automat. Lett., vol. 7, no. 4, pp. 11 537–11 544, 2022

work page 2022

Show all 32 references

[9]

Continuous-time stereo-inertial odometry,

D. Hug, P. B ¨anninger, I. Alzugaray, and M. Chli, “Continuous-time stereo-inertial odometry,”IEEE Robot. Automat. Lett., vol. 7, no. 3, pp. 6455–6462, 2022

2022
[10]

CT-ICP: Real-time elastic LiDAR odometry with loop closure,

P. Dellenbach, J.-E. Deschaud, B. Jacquet, and F. Goulette, “CT-ICP: Real-time elastic LiDAR odometry with loop closure,” inProc. IEEE Int. Conf. Robot. Automat., 2022, pp. 5580–5586

2022
[11]

PL-VIO: Tightly cou- pled monocular visual-inertial odometry using point and line features,

Y . He, J. Zhao, Y . Guo, W. He, and K. Yuan, “PL-VIO: Tightly cou- pled monocular visual-inertial odometry using point and line features,” Sensors, vol. 18, no. 4, p. 1159, 2018

2018
[12]

SPVIO: Pose-only visual-inertial odometry with state transformation and visual- deprived correction,

X. Du, L. Zhang, C. Ji, X. Luo, M. Wang, W. Wu, and J. Mao, “SPVIO: Pose-only visual-inertial odometry with state transformation and visual- deprived correction,”IEEE Internet of Things Journal, 2025

2025
[13]

Robust monocular vision- based 3D feature point correction leveraging IMU motion for visual- inertial odometry in agricultural autonomous applications,

Q. D. Tran, Q. H. Hoang, and G.-W. Kim, “Robust monocular vision- based 3D feature point correction leveraging IMU motion for visual- inertial odometry in agricultural autonomous applications,”IEEE Access, 2025

2025
[14]

DynaVINS++: Robust visual-inertial state estimator in dynamic environments by adaptive truncated least squares and stable state recovery,

S. Song, H. Lim, A. J. Lee, and H. Myung, “DynaVINS++: Robust visual-inertial state estimator in dynamic environments by adaptive truncated least squares and stable state recovery,”IEEE Robot. Automat. Lett., vol. 9, no. 10, pp. 9127–9134, 2024

2024
[15]

EqVIO: An equivariant filter for visual- inertial odometry,

P. van Goor and R. Mahony, “EqVIO: An equivariant filter for visual- inertial odometry,”IEEE Trans. Robot., vol. 39, no. 5, pp. 3567–3585, 2023

2023
[16]

Schurvins: Schur complement-based lightweight visual inertial navigation system,

Y . Fan, T. Zhao, and G. Wang, “Schurvins: Schur complement-based lightweight visual inertial navigation system,” inProc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., 2024, pp. 17 964–17 973

2024
[17]

Continuous-time spline visual-inertial odometry,

J. Mo and J. Sattar, “Continuous-time spline visual-inertial odometry,” arXiv preprint arXiv:2109.09035, 2021

2021 arXiv
[18]

Continuous-time state estimation methods in robotics: A survey,

W. Talbot, J. Nubert, T. Tuna, C. Cadena, F. D ¨umbgen, J. Tordesillas, T. D. Barfoot, and M. Hutter, “Continuous-time state estimation methods in robotics: A survey,”IEEE Trans. Robot., vol. 41, pp. 4975–4999, 2025

2025
[19]

CT-UIO: Continuous-time UWB-inertial-odometer localiza- tion using non-uniform B-spline with fewer anchors,

J. Sun, W. Sun, G. Zhang, K. Yang, S. Li, X. Meng, N. Deng, and C. Tan, “CT-UIO: Continuous-time UWB-inertial-odometer localiza- tion using non-uniform B-spline with fewer anchors,”arXiv preprint arXiv:2502.06287, 2025

2025
[20]

UV-SLAM: Unconstrained line-based SLAM using vanishing points for structural mapping,

H. Lim, J. Jeon, and H. Myung, “UV-SLAM: Unconstrained line-based SLAM using vanishing points for structural mapping,”IEEE Robot. Automat. Lett., vol. 7, no. 2, pp. 1518–1525, 2022

2022
[21]

DiT-SLAM: Real- time dense visual-inertial SLAM with implicit depth representation and tightly-coupled graph optimization,

M. Zhao, D. Zhou, X. Song, X. Chen, and L. Zhang, “DiT-SLAM: Real- time dense visual-inertial SLAM with implicit depth representation and tightly-coupled graph optimization,”Sensors, vol. 22, no. 9, p. 3389, 2022

2022
[22]

D-VINS: Dynamic adaptive visual-inertial SLAM with IMU prior and semantic constraints in dynamic scenes,

Y . Sun, Q. Wang, C. Yan, Y . Feng, R. Tan, X. Shi, and X. Wang, “D-VINS: Dynamic adaptive visual-inertial SLAM with IMU prior and semantic constraints in dynamic scenes,”Remote Sensing, vol. 15, no. 15, p. 3881, 2023

2023
[23]

GaussianFlow SLAM: Monocular Gaussian splatting SLAM guided by GaussianFlow,

D.-U. Seo, J. Jeon, E. M. Lee, and H. Myung, “GaussianFlow SLAM: Monocular Gaussian splatting SLAM guided by GaussianFlow,”IEEE Robot. Automat. Lett., vol. 11, no. 6, pp. 7644–7651, 2026

2026
[24]

AIM-SLAM: Dense monocular SLAM via adaptive and informative multi-view keyframe prioritization with foundation model,

J. Jeon, D.-U. Seo, E. M. Lee, and H. Myung, “AIM-SLAM: Dense monocular SLAM via adaptive and informative multi-view keyframe prioritization with foundation model,” inProc. IEEE Int. Conf. Robot. Automat., 2026, pp. 18 530–18 537

2026
[25]

Visual-inertial SLAM as simple as A, B, VINS,

N. Merrill and G. Huang, “Visual-inertial SLAM as simple as A, B, VINS,”arXiv preprint arXiv:2406.05969, 2024

2024 arXiv
[26]

MSCKF-DVIO: Multi- state constraint Kalman filter based RGB-D visual-inertial odometry with spline interpolation and nonholonomic constraint,

K. Jung, J. Song, S. Seong, and H. Myung, “MSCKF-DVIO: Multi- state constraint Kalman filter based RGB-D visual-inertial odometry with spline interpolation and nonholonomic constraint,” inProc. Int. Conf. Ubiquti. Robot., 2024, pp. 558–565

2024
[27]

Ceres solver: Tutorial & reference,

S. Agarwal, K. Mierleet al., “Ceres solver: Tutorial & reference,” Google Inc, vol. 2, no. 72, p. 8, 2012

2012
[28]

Run your visual- inertial odometry on NVIDIA Jetson: Benchmark tests on a micro aerial vehicle,

J. Jeon, S. Jung, E. Lee, D. Choi, and H. Myung, “Run your visual- inertial odometry on NVIDIA Jetson: Benchmark tests on a micro aerial vehicle,”IEEE Robot. Automat. Lett., vol. 6, no. 3, pp. 5332–5339, 2021

2021
[29]

Hilti-oxford dataset: A millimeter-accurate benchmark for simultaneous localization and mapping,

L. Zhang, M. Helmberger, L. F. T. Fu, D. Wisth, M. Camurri, D. Scara- muzza, and M. Fallon, “Hilti-oxford dataset: A millimeter-accurate benchmark for simultaneous localization and mapping,”IEEE Robot. Automat. Lett., vol. 8, no. 1, pp. 408–415, 2022

2022
[30]

PALoc: Advancing SLAM benchmarking with prior-assisted 6-DoF trajectory generation and uncertainty estimation,

X. Hu, L. Zheng, J. Wu, R. Geng, Y . Yu, H. Wei, X. Tang, L. Wang, J. Jiao, and M. Liu, “PALoc: Advancing SLAM benchmarking with prior-assisted 6-DoF trajectory generation and uncertainty estimation,” IEEE/ASME Trans. Mechatron., vol. 29, no. 6, pp. 4297–4308, 2024

2024
[31]

Large-scale LiDAR SLAM with factor graph optimization on high- level geometric features,

K. ´Cwian, M. R. Nowicki, J. Wietrzykowski, and P. Skrzypczy ´nski, “Large-scale LiDAR SLAM with factor graph optimization on high- level geometric features,”Sensors, vol. 21, no. 10, p. 3445, 2021

2021
[32]

S4-SLAM: A real-time 3D LiDAR SLAM system for ground/watersurface multi-scene outdoor applications,

B. Zhou, Y . He, K. Qian, X. Ma, and X. Li, “S4-SLAM: A real-time 3D LiDAR SLAM system for ground/watersurface multi-scene outdoor applications,”Autonomous Robots, vol. 45, no. 1, pp. 77–98, 2021

2021

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

[1] [1]

Robust stereo visual-inertial odometry for fast autonomous flight,

K. Sun, K. Mohta, B. Pfrommer, M. Watterson, S. Liu, Y . Mulgaonkar, C. J. Taylor, and V . Kumar, “Robust stereo visual-inertial odometry for fast autonomous flight,”IEEE Robot. Automat. Lett., vol. 3, no. 2, pp. 965–972, 2018

work page 2018

[2] [2]

VINS-Mono: A robust and versatile monocular visual-inertial state estimator,

T. Qin, P. Li, and S. Shen, “VINS-Mono: A robust and versatile monocular visual-inertial state estimator,”IEEE Trans. Robot., vol. 34, no. 4, pp. 1004–1020, 2018

2018

[3] [3]

PL-VINS: Real-time monocular visual-inertial SLAM with point and line,

Q. Fu, J. Wang, H. Yu, I. Ali, F. Guo, and H. Zhang, “PL-VINS: Real-time monocular visual-inertial SLAM with point and line,”arXiv preprint arXiv:2009.07462, 2020

arXiv 2009

[4] [4]

√ vins: Robust and ultra- fast square-root filter-based 3D motion tracking,

Y . Peng, C. Chen, K. Wu, and G. Huang, “ √ vins: Robust and ultra- fast square-root filter-based 3D motion tracking,”IEEE Trans. Robot., vol. 41, pp. 6570–6589, 2025

work page 2025

[5] [5]

Continuous-time batch estimation using temporal basis functions,

P. Furgale, T. D. Barfoot, and G. Sibley, “Continuous-time batch estimation using temporal basis functions,” inProc. IEEE Int. Conf. Robot. Automat., 2012, pp. 2088–2095

work page 2012

[6] [6]

Continuous- discrete extended Kalman filter on matrix Lie groups using concentrated Gaussian distributions,

G. Bourmaud, R. M ´egret, M. Arnaudon, and A. Giremus, “Continuous- discrete extended Kalman filter on matrix Lie groups using concentrated Gaussian distributions,”J. Math. Imaging Vis., vol. 51, no. 1, pp. 209– 228, 2015

work page 2015

[7] [7]

Continuous- time visual-inertial odometry for event cameras,

E. Mueggler, G. Gallego, H. Rebecq, and D. Scaramuzza, “Continuous- time visual-inertial odometry for event cameras,”IEEE Trans. Robot., vol. 34, no. 6, pp. 1425–1440, 2018

work page 2018

[8] [8]

Ctrl-VIO: Continuous-time visual-inertial odometry for rolling shutter cameras,

X. Lang, J. Lv, J. Huang, Y . Ma, Y . Liu, and X. Zuo, “Ctrl-VIO: Continuous-time visual-inertial odometry for rolling shutter cameras,” IEEE Robot. Automat. Lett., vol. 7, no. 4, pp. 11 537–11 544, 2022

work page 2022