Pith. sign in

REVIEW 3 major objections 5 minor 44 references

Event-based Stereo Visual-Inertial Odometry with Voxel Map

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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

Pith's one-line read Voxel-organized map points cut event-camera odometry error and runtime below existing systems.

desk verdict Credible fast event-based VIO with strong benchmark numbers, but the paper's causal story for the two voxel mechanisms is contradicted by its own ablation table and needs a careful revision. read the letter →

arxiv 2506.23078 v1 pith:RXJVTS2T submitted 2025-06-29 cs.RO

Zhaoxing Zhang , Xiaoxiang Wang , Chengliang Zhang , Yangyang Guo , Zikang Yuan , Xin Yang This is my paper
classification cs.RO
keywords eventcameravisual-inertialodometrystereovisionvoxelmappointselectionnoiseresilienceMSCKFtimesurface
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

Voxel-ESVIO is an event-based stereo visual-inertial odometry system that claims event-camera noise can be handled at the map level rather than only at the tracking level. The paper proposes dividing 3D space into voxels and, for each current frame, selecting map points from voxels that intersect the camera frustum and their second-order neighbors, on the theory that these points combine observability with resistance to event noise. A companion voxel-aware management pipeline filters newly triangulated points by temporal consistency, spatial proximity, and a five-point-per-voxel cap to keep the map clean and uniform. Evaluated on 19 sequences from the VECtor, DSEC, and RPG benchmarks, the system reports the lowest Absolute Trajectory Error on every sequence and a per-stereo-event-pair runtime of 15-30 milliseconds, versus 38-195 milliseconds for open baselines. If correct, the work shows that spatial discretization of the map is a low-cost way to suppress event noise that propagates into state estimation.

What carries the argument

The central machinery is the voxel map: 3D space is partitioned into voxels, each storing map points that carry a feature ID, a global 3D position, a host keyframe, and a host voxel. Two strategies run on this structure. Voxel-based point selection takes triangulated 3D points from the current stereo frame, indexes their voxels, restricts access to voxels intersecting the current camera frustum, and also pulls points from second-order topological neighbors, so the optimizer receives spatially diverse points with high expected observation likelihood. Voxel-aware point management registers new points through three cascaded filters: keep only the most frequently tracked points per voxel (temporal consistency), cull points that fall too close to existing ones (spatial proximity), and cap each voxel at five points (voxel capacity). This per-voxel bookkeeping is what lets the system discard noise-generated points without an explicit outlier-removal stage.

What would settle it

Run Voxel-ESVIO on a sequence where a large foreground object occludes most of the frustum-intersecting voxels; if the system's ATE stays low even though many selected map points are behind the occluder, or if a version that explicitly checks point visibility gives the same accuracy, the claimed selection mechanism is not the cause of the reported gains.

Watch

Extended reading notes

Core claim

The paper's central claim is that a voxel-based map representation solves the map-point quality bottleneck in event-based stereo VIO. Event streams contain spurious points triggered by noise; naive global map management lets those false correspondences enter the optimizer and corrupt pose estimates. Voxel-ESVIO instead triangulates current feature correspondences, indexes the containing voxels, and feeds the state update only with points from frustum-intersecting voxels and their second-order topological neighbors, arguing that these are the points most likely to be observable and noise-resilient. It then registers new points through a three-stage cascade—temporal consistency validation, spatial proximity culling, and a five-point voxel capacity limit—so the map stays sparse, uniform, and free of transient artifacts. On all 19 sequences across the three benchmarks, it reports the lowest ATE, and its per-event-pair runtime stays between 15 and 30 ms, indicating that the voxel bookkeeping adds negligible overhead.

Load-bearing premise

The load-bearing assumption is that a map point is worth using for state updates simply because its voxel intersects the current camera frustum (or touches a voxel that does), and that this spatial proxy reliably stands in for true observability and noise resistance; the system never checks whether the point is actually visible or well triangulated.

Editorial extensions

If this is right

  • Voxel-level spatial organization is sufficient to make event-based stereo VIO accurate enough to outperform existing direct and indirect event systems on public benchmarks.
  • The reported 15-30 ms per stereo event pair means the voxel map adds little overhead, so noise filtering through spatial discretization is compatible with real-time operation.
  • The two-stage update—first estimating poses without map points, then refining with selected map points—benefits measurably from restricting the second update to frustum-relevant voxels.
  • Ablations show that removing either the voxel-based selection or the voxel-aware management increases ATE on most sequences, implying both strategies contribute to the gain.

Reading between the lines

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

  • The selection rule is a proxy for observability, not a measurement; a direct visibility check on selected points would be a natural next step and might matter most on occluded driving sequences.
  • The five-point-per-voxel cap is a fixed number; an adaptive cap that scales with scene density or voxel size could generalize better across indoor and automotive environments.
  • The voxel map idea should port to monocular event-inertial systems, where false triangulations are even harder to detect without a second camera; the paper tests only the stereo configuration.
  • One could test the noise-resilience claim directly by comparing the fraction of selected points that survive to a later frame against a random-selection baseline; a higher survival rate would confirm the temporal-consistency filter is doing the work.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper proposes Voxel-ESVIO, an event-based stereo visual-inertial odometry system built on an MSCKF backend and a voxel map. The two claimed contributions are voxel-based point selection, which picks map points from frustum-intersecting voxels and their second-order neighbors, and voxel-aware point management, which applies temporal consistency, spatial proximity culling, and voxel capacity limits when registering new map points. The authors evaluate ATE against five baselines on VECtor, DSEC, and RPG, report per-module runtimes, and provide an ablation study. They claim the lowest ATE on all evaluated sequences and real-time processing of each stereo event pair in 15 to 30 ms.

Significance. If the empirical results hold, the paper offers a practically meaningful improvement: it reports the best ATE on all 19 tested sequences and substantially lower runtime than two open baselines. The voxel map organization is a plausible engineering contribution, and the evaluation across three public datasets is a strength. However, the paper's causal explanation for the accuracy gain is not supported by the reported ablation, and the central 'observation likelihood' claim is asserted rather than validated. The significance therefore depends on whether the authors can repair the ablation evidence and either justify the voxel heuristic with point-level validation or revise the claims to match the evidence.

major comments (3)
  1. [Sec. IV-D, Table V] The ablation table contradicts the claim that both proposed strategies yield consistent improvements. On hdr normal, the full system (0.059 m) is worse than the variant without voxel-aware point management (0.056 m); on City04 b, the full system (0.46 m) is worse than the variant without voxel-based point selection (0.43 m); on bin, disabling voxel-aware management ties the full system (0.020 m). The text in Sec. IV-D.1 and IV-D.2 states that each strategy improves accuracy across all test scenarios, which these rows falsify. The authors should report per-sequence differences, repeat runs with dispersion or significance measures, and explain the negative cases rather than asserting consistency.
  2. [Sec. III-E.3] The central attribution of accuracy to retrieving 'noise-resilient map points with the highest observation likelihood' is not established. The selection rule is purely geometric: a point is selected if its voxel intersects the current frustum or is a second-order neighbor of such a voxel, and no per-point frustum check, occlusion test, or triangulation-quality metric is computed. The 'w/o voxel-based point selection' control keeps the same point count but selects points sequentially from storage, so it does not isolate observability. The authors should either add a point-level validation or moderate the claim to a spatial-distribution heuristic and provide evidence that the distribution, rather than observability, drives the accuracy gain.
  3. [Sec. III-E.5] The voxel-aware point management pipeline introduces several unspecified thresholds and a fixed voxel capacity of five points, yet the ablation shows that this module can hurt accuracy on hdr normal. Without reporting the temporal-consistency criterion, the minimum spatial distance for culling, and a sensitivity analysis of the capacity limit, it is difficult to know whether the reported gains are robust or artifacts of particular parameter choices. The authors should disclose these thresholds and show how the results vary when each is changed.
minor comments (5)
  1. [Abstract and throughout] There are several typos and inconsistencies, including 'ensureing' in the abstract, 'datatests' in Sec. I, 'Ablation Stuty' in Sec. IV-D, 'frist' in Sec. II-B, and inconsistent capitalization of the RPG dataset name.
  2. [Table III and Sec. IV-C] The text says the maximum total processing time is 30 ms, but Table III reports 30.12 ms for City11 b; the rounding should be stated explicitly or the bound should be corrected to 31 ms.
  3. [Sec. II-B and Table II] The baseline naming is confusing: reference [15] and reference [16] are both titled 'ESVIO,' yet one is labeled ESIO and the other ESVIO in the tables. Please clarify the naming to avoid ambiguity.
  4. [Sec. IV-E] Fig. 4 is described as a 'quantitative evaluation,' but it is a trajectory visualization; the text should say 'qualitative visualization' unless quantitative metrics are overlaid.
  5. [Sec. III-E.3] The measurement model in Eq. (13) is written as a function from R^3 to R^2, but the argument is the full state sub-vector; please make the notation consistent with the Jacobian H used in Eqs. (14)-(16).

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the system is validated against external benchmarks and no load-bearing claim reduces to its own inputs.

full rationale

The paper's derivation chain is self-contained. The voxel-based point selection (Sec. III-E.3) and voxel-aware point management (Sec. III-E.5) are implemented mechanisms, not quantities defined in terms of the ATE outcomes they are used to explain. The state updates in Secs. III-E.2 through III-E.4 follow a standard MSCKF-style formulation; the second update uses map points as additional measurements, and no equation defines the output trajectory as an input. Accuracy is evaluated with the external evo toolkit [41] against ground truth on VECtor, DSEC, and RPG, so the reported ATE values are externally measured rather than constructed by the method. Reference [6] (Voxel-SVIO) shares authors with this paper, but it is cited only as an example of conventional VIO and is not invoked as the justification for the proposed voxel strategies or to exclude alternatives; accordingly it is not load-bearing. The ablation study in Table V does not uniformly support the text's claim of 'consistent improvements' (e.g., hdr normal: 0.056 without voxel-aware management vs. 0.059 full; City04 b: 0.43 without voxel-based selection vs. 0.46 full), but this is an internal-support/consistency issue rather than circular reasoning. No fitted parameter is renamed as a prediction, and no result is obtained by definition.

Assumptions & free parameters 6 free parameters · 4 assumptions · 0 invented entities

The paper introduces a voxel map data structure, not a new physical entity. The voxel map is a bookkeeping device whose behavior is fully determined by the specified fields. All load-bearing assumptions are modeling choices about event noise, the time-surface representation, and the observability proxy, plus unstated thresholds that act as free parameters.

free parameters (6)
  • Voxel size = unspecified
    Divides 3D space into voxels; value not given in the paper, though it controls how many points share a voxel and directly affects point selection and capacity culling behavior.
  • Voxel capacity = 5 points per voxel
    Set by hand in Sec. III-E.5; limits map density to maintain uniform distribution and efficiency, but no sensitivity analysis is provided.
  • Spatial proximity culling minimum distance = unspecified
    In Sec. III-E.5, points too close to existing points are removed; the threshold is not reported.
  • Temporal consistency threshold = unspecified
    In Sec. III-E.5, points must be tracked frequently within a voxel to be retained; the required number of observations is not specified.
  • Keyframe parallax threshold = 20 pixels
    Sec. III-E.6; a new keyframe is created when parallax exceeds 20 pixels; chosen without justification.
  • Time-surface decay constant eta = unspecified
    Eq. (1); the exponential decay time constant for the time-surface map, a tunable parameter affecting the event image representation.
assumptions (4)
  • standard math MSCKF measurement model and perspective projection (from [39], [40])
    Equations (13)-(16) and (17)-(20) rely on the multi-state constraint Kalman filter and standard camera projection without re-derivation.
  • domain assumption Event noise is temporally inconsistent
    Sec. III-E.5 states 'Due to the typically inconsistent nature of event noise', justifying the temporal consistency filter. If noise is persistent or structured, the filter could discard true points or keep noise.
  • domain assumption Time-surface maps with exponential decay preserve feature trackability
    Sec. III-B defines the time-surface map and the tracking module runs KLT on these maps; the paper does not validate this representation against alternatives.
  • ad hoc to paper Frustum membership plus second-order neighbor voxels is a valid proxy for observation likelihood
    Sec. III-E.3 selects map points from voxels intersecting the frustum and their second-order neighbors, without computing actual likelihoods, occlusion, or geometric quality.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Event-based Stereo Visual-Inertial Odometry with Voxel Map." pith.science (2026). https://pith.science/paper/RXJVTS2T

@misc{pith2026250623078,
  author       = {Pith},
  title        = {Pith review of: Event-based Stereo Visual-Inertial Odometry with Voxel Map},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RXJVTS2T}},
  note         = {Machine review of arXiv:2506.23078}
}
read the original abstract

The event camera, renowned for its high dynamic range and exceptional temporal resolution, is recognized as an important sensor for visual odometry. However, the inherent noise in event streams complicates the selection of high-quality map points, which critically determine the precision of state estimation. To address this challenge, we propose Voxel-ESVIO, an event-based stereo visual-inertial odometry system that utilizes voxel map management, which efficiently filter out high-quality 3D points. Specifically, our methodology utilizes voxel-based point selection and voxel-aware point management to collectively optimize the selection and updating of map points on a per-voxel basis. These synergistic strategies enable the efficient retrieval of noise-resilient map points with the highest observation likelihood in current frames, thereby ensureing the state estimation accuracy. Extensive evaluations on three public benchmarks demonstrate that our Voxel-ESVIO outperforms state-of-the-art methods in both accuracy and computational efficiency.

Figures

Figures reproduced from arXiv: 2506.23078 by the authors.

Figure 1
Figure 1. Illustration of event noise: Red and blue events indicate positive [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Illustration of voxel map management for map point organization. [PITH_FULL_IMAGE:figures/full_fig_p001_2.png] view at source ↗
Figure 3
Figure 3. System framework of Voxel-ESVIO, which comprises four main modules: the Event Processing Module, Tracking Module, MSCKF based [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Visualization comparison of estimated trajectory and groundtruth. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

44 extracted references · 36 canonical work pages

  1. [1]

    A 128×128 120 db 15 us latency asynchronous temporal contrast vision sensor,

    P. Lichtsteiner, C. Posch, and T. Delbruck, “A 128×128 120 db 15 us latency asynchronous temporal contrast vision sensor,” IEEE journal of solid-state circuits , vol. 43, no. 2, pp. 566–576, 2008

    work page 2008

  2. [2]

    Event- based vision: A survey,

    G. Gallego, T. Delbr ¨uck, G. Orchard, C. Bartolozzi, B. Taba, A. Censi, S. Leutenegger, A. J. Davison, J. Conradt, K. Daniilidis et al., “Event- based vision: A survey,” IEEE transactions on pattern analysis and machine intelligence, vol. 44, no. 1, pp. 154–180, 2020

    2020

  3. [3]

    Recent event camera innovations: A survey,

    B. Chakravarthi, A. A. Verma, K. Daniilidis, C. Fermuller, and Y . Yang, “Recent event camera innovations: A survey,”arXiv preprint arXiv:2408.13627, 2024

    arXiv 2024

  4. [4]

    Vins-mono: A robust and versatile monoc- ular visual-inertial state estimator,

    T. Qin, P. Li, and S. Shen, “Vins-mono: A robust and versatile monoc- ular visual-inertial state estimator,” IEEE transactions on robotics , vol. 34, no. 4, pp. 1004–1020, 2018

    work page 2018

  5. [5]

    Orb-slam3: An accurate open-source library for visual, visual–inertial, and multimap slam,

    C. Campos, R. Elvira, J. J. G. Rodr ´ıguez, J. M. Montiel, and J. D. Tard ´os, “Orb-slam3: An accurate open-source library for visual, visual–inertial, and multimap slam,” IEEE transactions on robotics , vol. 37, no. 6, pp. 1874–1890, 2021

    work page 2021

  6. [6]

    V oxel-svio: Stereo visual-inertial odometry based on voxel map,

    Z. Yuan, F. Lang, J. Deng, H. Luo, and X. Yang, “V oxel-svio: Stereo visual-inertial odometry based on voxel map,” IEEE Robotics and Automation Letters, 2025

    work page 2025

  7. [7]

    Orb-slam: A versatile and accurate monocular slam system,

    R. Mur-Artal, J. M. M. Montiel, and J. D. Tardos, “Orb-slam: A versatile and accurate monocular slam system,” IEEE transactions on robotics, vol. 31, no. 5, pp. 1147–1163, 2015

    2015

  8. [8]

    Openvins: A research platform for visual-inertial estimation,

    P. Geneva, K. Eckenhoff, W. Lee, Y . Yang, and G. Huang, “Openvins: A research platform for visual-inertial estimation,” in 2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2020, pp. 4666–4672

    work page 2020

Show all 44 references
  1. [9]

    Sr-lio: Lidar-inertial odometry with sweep reconstruction,

    Z. Yuan, F. Lang, T. Xu, and X. Yang, “Sr-lio: Lidar-inertial odometry with sweep reconstruction,” in 2024 IEEE/RSJ International Confer- ence on Intelligent Robots and Systems (IROS) . IEEE, 2024, pp. 7862–7869

    2024

  2. [10]

    Sr-livo: Lidar- inertial-visual odometry and mapping with sweep reconstruction,

    Z. Yuan, J. Deng, R. Ming, F. Lang, and X. Yang, “Sr-livo: Lidar- inertial-visual odometry and mapping with sweep reconstruction,” IEEE Robotics and Automation Letters , 2024

    2024

  3. [11]

    Sdv-loam: Semi- direct visual–lidar odometry and mapping,

    Z. Yuan, Q. Wang, K. Cheng, T. Hao, and X. Yang, “Sdv-loam: Semi- direct visual–lidar odometry and mapping,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 9, pp. 11 203– 11 220, 2023

    2023

  4. [12]

    Semi-elastic lidar-inertial odometry,

    Z. Yuan, F. Lang, T. Xu, R. Ming, C. Zhao, and X. Yang, “Semi-elastic lidar-inertial odometry,” arXiv preprint arXiv:2307.07792 , 2023

    2023 arXiv

  5. [13]

    Evo: A geometric approach to event-based 6-dof parallel tracking and mapping in real time,

    H. Rebecq, T. Horstsch ¨afer, G. Gallego, and D. Scaramuzza, “Evo: A geometric approach to event-based 6-dof parallel tracking and mapping in real time,” IEEE Robotics and Automation Letters , vol. 2, no. 2, pp. 593–600, 2016

    2016

  6. [14]

    Feature-based event stereo visual odometry,

    A. Hadviger, I. Cvi ˇsi´c, I. Markovi ´c, S. Vra ˇzi´c, and I. Petrovi ´c, “Feature-based event stereo visual odometry,” in 2021 European Conference on Mobile Robots (ECMR) . IEEE, 2021, pp. 1–6

    2021

  7. [15]

    Esvio: Event-based stereo visual inertial odometry,

    P. Chen, W. Guan, and P. Lu, “Esvio: Event-based stereo visual inertial odometry,” IEEE Robotics and Automation Letters , vol. 8, no. 6, pp. 3661–3668, 2023

    2023

  8. [16]

    Esvio: Event-based stereo visual- inertial odometry,

    Z. Liu, D. Shi, R. Li, and S. Yang, “Esvio: Event-based stereo visual- inertial odometry,” Sensors, vol. 23, no. 4, p. 1998, 2023

    1998

  9. [17]

    Event-based stereo visual odom- etry,

    Y . Zhou, G. Gallego, and S. Shen, “Event-based stereo visual odom- etry,” IEEE Transactions on Robotics , vol. 37, no. 5, pp. 1433–1450, 2021

    2021

  10. [18]

    Imu-aided event-based stereo visual odometry,

    J. Niu, S. Zhong, and Y . Zhou, “Imu-aided event-based stereo visual odometry,” in 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024, pp. 11 977–11 983

    2024

  11. [19]

    Esvo2: Direct visual-inertial odometry with stereo event cameras,

    J. Niu, S. Zhong, X. Lu, S. Shen, G. Gallego, and Y . Zhou, “Esvo2: Direct visual-inertial odometry with stereo event cameras,” arXiv preprint arXiv:2410.09374, 2024

    2024 arXiv

  12. [20]

    Event-based visual in- ertial odometry,

    A. Zihao Zhu, N. Atanasov, and K. Daniilidis, “Event-based visual in- ertial odometry,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 5391–5399

    2017

  13. [21]

    Low-latency visual odometry using event-based feature tracks,

    B. Kueng, E. Mueggler, G. Gallego, and D. Scaramuzza, “Low-latency visual odometry using event-based feature tracks,” in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2016, pp. 16–23

    2016

  14. [22]

    Speed invariant time surface for learning to detect corner points with event-based cameras,

    J. Manderscheid, A. Sironi, N. Bourdis, D. Migliore, and V . Lepetit, “Speed invariant time surface for learning to detect corner points with event-based cameras,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 10 245–10 254

    2019

  15. [23]

    luvharris: A practical corner detector for event-cameras,

    A. Glover, A. Dinale, L. D. S. Rosa, S. Bamford, and C. Bartolozzi, “luvharris: A practical corner detector for event-cameras,”IEEE Trans- actions on Pattern Analysis and Machine Intelligence , vol. 44, no. 12, pp. 10 087–10 098, 2021

    2021

  16. [24]

    Pl-evio: Robust monocular event-based visual inertial odometry with point and line features,

    W. Guan, P. Chen, Y . Xie, and P. Lu, “Pl-evio: Robust monocular event-based visual inertial odometry with point and line features,” IEEE Transactions on Automation Science and Engineering , vol. 21, no. 4, pp. 6277–6293, 2023

    2023

  17. [25]

    Asynchronous corner detection and track- ing for event cameras in real time,

    I. Alzugaray and M. Chli, “Asynchronous corner detection and track- ing for event cameras in real time,” IEEE Robotics and Automation Letters, vol. 3, no. 4, pp. 3177–3184, 2018

    2018

  18. [26]

    Ace: An efficient asynchronous corner tracker for event cam- eras,

    ——, “Ace: An efficient asynchronous corner tracker for event cam- eras,” in 2018 International Conference on 3D Vision (3DV) . IEEE, 2018, pp. 653–661

    2018

  19. [27]

    Ul- timate slam? combining events, images, and imu for robust visual slam in hdr and high-speed scenarios,

    A. R. Vidal, H. Rebecq, T. Horstschaefer, and D. Scaramuzza, “Ul- timate slam? combining events, images, and imu for robust visual slam in hdr and high-speed scenarios,” IEEE Robotics and Automation Letters, vol. 3, no. 2, pp. 994–1001, 2018

    2018

  20. [28]

    Parallel tracking and mapping for small ar workspaces,

    G. Klein and D. Murray, “Parallel tracking and mapping for small ar workspaces,” in 2007 6th IEEE and ACM international symposium on mixed and augmented reality . IEEE, 2007, pp. 225–234

    2007

  21. [29]

    Real-time 3d reconstruc- tion and 6-dof tracking with an event camera,

    H. Kim, S. Leutenegger, and A. J. Davison, “Real-time 3d reconstruc- tion and 6-dof tracking with an event camera,” in European conference on computer vision . Springer, 2016, pp. 349–364

    2016

  22. [30]

    evo: Python package for the evaluation of odometry and slam

    M. Grupp, “evo: Python package for the evaluation of odometry and slam.” https://github.com/MichaelGrupp/evo, 2017

    2017

  23. [31]

    Emvs: Event-based multi-view stereo,

    H. Rebecq, G. Gallego, and D. Scaramuzza, “Emvs: Event-based multi-view stereo,” 2016

    2016

  24. [32]

    Fast event-based harris corner detection exploiting the advantages of event-driven cameras,

    V . Vasco, A. Glover, and C. Bartolozzi, “Fast event-based harris corner detection exploiting the advantages of event-driven cameras,” in 2016 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE, 2016, pp. 4144–4149

    2016

  25. [33]

    Fa-harris: A fast and asynchronous corner detector for event cameras,

    R. Li, D. Shi, Y . Zhang, K. Li, and R. Li, “Fa-harris: A fast and asynchronous corner detector for event cameras,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2019, pp. 6223–6229

    2019

  26. [34]

    Monocular event visual inertial odometry based on event-corner using sliding windows graph-based optimization,

    W. Guan and P. Lu, “Monocular event visual inertial odometry based on event-corner using sliding windows graph-based optimization,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2022, pp. 2438–2445

    2022

  27. [35]

    Real-time visual- inertial odometry for event cameras using keyframe-based nonlinear optimization,

    H. Rebecq, T. Horstschaefer, and D. Scaramuzza, “Real-time visual- inertial odometry for event cameras using keyframe-based nonlinear optimization,” 2017

    2017

  28. [36]

    Hots: a hierarchy of event-based time-surfaces for pattern recogni- tion,

    X. Lagorce, G. Orchard, F. Galluppi, B. E. Shi, and R. B. Benosman, “Hots: a hierarchy of event-based time-surfaces for pattern recogni- tion,” IEEE transactions on pattern analysis and machine intelligence , vol. 39, no. 7, pp. 1346–1359, 2016

    2016

  29. [37]

    An iterative image registration technique with an application to stereo vision,

    B. D. Lucas and T. Kanade, “An iterative image registration technique with an application to stereo vision,” in IJCAI’81: 7th international joint conference on Artificial intelligence , vol. 2, 1981, pp. 674–679

    1981

  30. [38]

    Good features to track,

    J. Shi and Tomasi, “Good features to track,” in 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition , 1994, pp. 593–600

    1994

  31. [39]

    A multi-state constraint kalman filter for vision-aided inertial navigation,

    A. I. Mourikis and S. I. Roumeliotis, “A multi-state constraint kalman filter for vision-aided inertial navigation,” in Proceedings 2007 IEEE international conference on robotics and automation . IEEE, 2007, pp. 3565–3572

    2007

  32. [40]

    Indirect kalman filter for 3d attitude estimation,

    N. Trawny and S. I. Roumeliotis, “Indirect kalman filter for 3d attitude estimation,” University of Minnesota, Dept. of Comp. Sci. & Eng., Tech. Rep, vol. 2, p. 2005, 2005

    2005

  33. [41]

    A benchmark for the evaluation of rgb-d slam systems,

    J. Sturm, N. Engelhard, F. Endres, W. Burgard, and D. Cremers, “A benchmark for the evaluation of rgb-d slam systems,” in 2012 IEEE/RSJ international conference on intelligent robots and systems . IEEE, 2012, pp. 573–580

    2012

  34. [42]

    Vector: A versatile event-centric benchmark for multi-sensor slam,

    L. Gao, Y . Liang, J. Yang, S. Wu, C. Wang, J. Chen, and L. Kneip, “Vector: A versatile event-centric benchmark for multi-sensor slam,” IEEE Robotics and Automation Letters , vol. 7, no. 3, pp. 8217–8224, 2022

    2022

  35. [43]

    Dsec: A stereo event camera dataset for driving scenarios,

    M. Gehrig, W. Aarents, D. Gehrig, and D. Scaramuzza, “Dsec: A stereo event camera dataset for driving scenarios,” IEEE Robotics and Automation Letters, vol. 6, no. 3, pp. 4947–4954, 2021

    2021

  36. [44]

    Semi-dense 3d reconstruction with a stereo event camera,

    Y . Zhou, G. Gallego, H. Rebecq, L. Kneip, H. Li, and D. Scara- muzza, “Semi-dense 3d reconstruction with a stereo event camera,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 235–251

    2018

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.