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arxiv: 2605.17264 · v1 · pith:FVVNTUXQnew · submitted 2026-05-17 · 💻 cs.RO

Stretch-ICP: A Continuous-Trajectory Registration and Deskewing Algorithm in Scenarios of Aggressive Motions

Simon-Pierre Desch\^enes , Veronica Vannini , Philippe Gigu\`ere , Fran\c{c}ois Pomerleau This is my paper

Pith reviewed 2026-05-20 13:20 UTC · model grok-4.3

classification 💻 cs.RO
keywords lidar registrationdeskewingcontinuous trajectoryaggressive motionsSLAMICPstate estimationTIGS dataset
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The pith

Stretch-ICP reconstructs smoother 6-DOF trajectories under aggressive motions by modeling continuous sensor paths instead of static scans.

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

The paper presents Stretch-ICP as a registration and deskewing method for lidar scans collected while a robot undergoes extreme accelerations and rotations, such as tumbling down a hill. Classical ICP assumes the sensor stays still during each scan, which breaks down when motions are fast enough to distort the data. Stretch-ICP instead fits a continuous trajectory through the scan, producing smoother position and orientation estimates. The authors show this approach cuts linear and angular velocity errors by 95.2 percent and 94.8 percent at scan boundaries on their new TIGS dataset of high-speed tumbling. A reader would care because reliable state estimation in rough or slippery conditions is a prerequisite for autonomous robots operating outside controlled lab floors.

Core claim

Stretch-ICP is a registration and deskewing algorithm that reconstructs continuous 6-DOF trajectories from motion-distorted lidar scans. On the TIGS dataset of mechanical tumbling with angular speeds up to four times higher than prior collections, it reduces linear velocity error by 95.2 percent and angular velocity error by 94.8 percent at scan boundaries relative to classical ICP.

What carries the argument

Stretch-ICP, a continuous-trajectory version of ICP registration that solves for a time-varying pose during each scan rather than a single static transformation.

If this is right

  • Smoother 6-DOF trajectory estimates become available even when lidar scans are collected during tumbling or sharp accelerations.
  • Linear velocity error at scan boundaries falls by 95.2 percent compared with standard ICP.
  • Angular velocity error at scan boundaries falls by 94.8 percent compared with standard ICP.
  • Lidar-inertial state estimation gains robustness and consistency when both Stretch-ICP and saturation-aware angular velocity estimation are used together.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The continuous-trajectory model could be combined with other motion-distortion corrections to handle fast-moving platforms beyond the tested tumbling case.
  • Robots navigating uneven outdoor terrain might maintain lower drift in pose estimates if the method replaces discrete-scan ICP in their mapping pipeline.
  • The same deskewing principle may extend to camera or radar data collected under high dynamic conditions.

Load-bearing premise

The large error reductions measured on the mechanical tumbling rig will appear on actual robots that experience comparable aggressive motions.

What would settle it

A side-by-side test on a real mobile robot executing rapid turns or drops, measuring whether velocity error at scan boundaries still drops by roughly 95 percent when Stretch-ICP replaces classical ICP.

Figures

Figures reproduced from arXiv: 2605.17264 by Fran\c{c}ois Pomerleau, Philippe Gigu\`ere, Simon-Pierre Desch\^enes, Veronica Vannini.

Figure 1
Figure 1. Figure 1: Example of reconstructed trajectories obtained with a SLAM framework using ICP (purple) and Stretch-ICP (green) as registration algorithms. For clarity, the 3D trajectories are projected onto the X-Z plane, and the dotted line indicates the ground-truth trajectory. The black arrows indicate the direction of motion through time. The zoomed-in views on the left show a scan boundary, where ‘End’ marks the end… view at source ↗
Figure 2
Figure 2. Figure 2: Angular speed over time for the saturated gyroscope axis during a tumbling event. Light gray zones indicate saturation periods. The saturated gyroscope measurements are shown in blue, the ground-truth angular speeds in orange, and the norm of the measured acceleration in green. The manufacturer-specified gyroscope saturation point is shown in dark gray. Examples of collision and free-fall events are highli… view at source ↗
Figure 3
Figure 3. Figure 3: Illustration of the main quantities used in the angular velocity estimation method. The COM is shown as a blue dot and the IMU as a red square. The rotation axis e is assumed to pass through the COM; it is shown as a solid line. The IMU rotates around e along the red circle. The vector t joins the COM to the IMU, and the vector r joins the rotation axis to the IMU; both geometric constructs are shown with … view at source ↗
Figure 4
Figure 4. Figure 4: Toy example comparing Stretch-ICP with ICP. The light blue points represent a map, while the yellow points represent a scan acquired by a moving lidar. The orange dot and blue square represent the start and end positions of two consecutive intra-scan trajectories. The gray line represents the previous intra-scan trajectory, and the black line the current one. The green arrows indicate the matches used by t… view at source ↗
Figure 5
Figure 5. Figure 5: Illustration of the steps of Stretch-ICP. The algorithm takes as input the lidar state xt at the start of the scan, the IMU measurements [ tω, t+qω, · · · ] and [ ta, t+qa, · · · ] recorded during the scan, the reading point cloud P, and the reference point cloud Q. The output is the lidar trajectory [ tx, t+qx, · · · , t+sx] during the scan. Our novel Data Stretcher module is highlighted in red, while the… view at source ↗
Figure 6
Figure 6. Figure 6: Factor graph used to optimize the intra-scan trajectory. The circles represent the intra-scan trajectory states x, and the black squares represent the factors B, I, and S. 5. Experimental Setup To validate the improvements reached through our methods while minimizing damage to a full robot, we created a rugged perception rig, shown in the top right of [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Composite image showing our localization system tumbling down a steep hill in one of the 32 runs of the TIGS dataset. The inset photo shows our rugged perception rig, with the numbers in the red circles corresponding to (1) XSens MTi-30 IMU, (2) VectorNav VN-100 IMU, (3) RoboSense RS-16 lidar, and (4) Raspberry Pi 4. All experiments were executed offline on a Lenovo ThinkPad P52s laptop (Lenovo, Morrisvill… view at source ↗
Figure 8
Figure 8. Figure 8: Density map of the TIGS dataset. The grayscale represents the number of data points ac￾quired at the specific angular speeds and linear accelerations. The outlines represent the distributions in linear accelerations and angular speeds for similar datasets. The KITTI dataset is shown in green, the Newer College dataset is indicated in orange, and the Hilti-Oxford dataset is illustrated in red. The dashed li… view at source ↗
Figure 9
Figure 9. Figure 9: Photo of our perception rig inside the room where the HRMC dataset was recorded. Small reflective spheres were fixed on the rig to track its 6-DOF trajectory using a Vicon Mo-Cap system. Ropes were attached to both sides of the rig to allow the operators to move the rig rapidly while avoiding occluding the Mo-Cap system’s cameras with their bodies. To better contextualize the motion regimes covered by our … view at source ↗
Figure 10
Figure 10. Figure 10: Qualitative and quantitative results on angular speed estimates for SAAVE. (Left) An example of the angular velocity over time for the saturated gyroscope (MTi-30) axis for one run of the TIGS dataset. The measurements from the MTi-30 gyroscope are shown in dashed blue, the ground truth measurements from a VN-100 gyroscope are indicated in dashed orange, and the angular speeds estimated with SAAVE using M… view at source ↗
Figure 11
Figure 11. Figure 11: Localization error for all runs in the TIGS dataset. (Left) The cumulative probability of observing a given translation error is shown on the left plot. (Right) The cumulative probability of observing a rotation error. The blue, purple, and pink lines represent the cumulative probability of localization errors for ICP-SLAM, SAAVE-ICP-SLAM, and Point-LIO, respectively. Note that the errors on the x-axis ar… view at source ↗
Figure 12
Figure 12. Figure 12: Side view of the ground-truth map built for the TIGS dataset. The color map is proportional to the point height. Mapping outliers from the fourteenth run of the TIGS dataset are displayed in red. (Top) A map showing the outliers of ICP-SLAM. (Bottom) The same map showing the outliers of SAAVE-ICP-SLAM. A point is considered an outlier if it is farther than 0.25 m from the ground-truth map. 6.4. Improving … view at source ↗
Figure 13
Figure 13. Figure 13: Error on the angular speed derived from estimated trajectories on all runs of the TIGS dataset. In purple is the angular speed error of the trajectory estimated by SAAVE-ICP-SLAM, and in green is the error of the trajectory estimated by SAAVE-Stretch-SLAM. The curves represent the mean of the error distributions, and the shaded areas represent the standard deviation around the mean. As can be seen, the SA… view at source ↗
Figure 14
Figure 14. Figure 14: Distribution of errors on the linear speeds derived from trajectories estimated by the different methods on all runs of the HRMC dataset. In purple is the error of the linear speeds estimated by SAAVE-ICP-SLAM, and in green is the error of the linear speeds estimated by SAAVE￾Stretch-SLAM. (Left) The curves represent the distribution of linear speed errors below 3 m/s. (Right) The bars represent the numbe… view at source ↗
Figure 15
Figure 15. Figure 15: Errors created by discontinuities at the beginning of scans on all runs of the HRMC dataset. (Left) The linear speed errors. (Right) Angular speed errors. The purple boxes represent the error of SAAVE-ICP-SLAM, and the green boxes represent the error of SAAVE-Stretch-SLAM. The black line indicates the median of the error distributions, and the bottom and top of the boxes represent the first and third quar… view at source ↗
Figure 16
Figure 16. Figure 16: Localization error for all runs in the TIGS dataset. (Left) The cumulative probability of observing a given translation error is shown on the left plot. (Right) The cumulative probability of observing a rotation error. The purple, green, and pink lines represent the cumulative probability of localization errors for SAAVE-ICP-SLAM, SAAVE-Stretch-SLAM, and Point-LIO, respectively. Note that the errors on th… view at source ↗
read the original abstract

Robust robotic autonomy remains challenging in complex environments, where loss of stability on uneven or slippery terrain can induce extreme accelerations and angular velocities. Such motions corrupt sensor measurements and degrade state estimation, motivating the need for improved algorithmic robustness. To investigate this issue, we introduce the Tumbling-Induced Gyroscope Saturation (TIGS) dataset, which consists of recordings from a mechanical lidar and an Inertial Measurement Unit (IMU) tumbling down a hill. The dataset contains angular speeds up to four times higher than those in similar datasets and is publicly available. We then propose two complementary methods to improve Simultaneous Localization And Mapping (SLAM) robustness and evaluate them on TIGS. First, Saturation-Aware Angular Velocity Estimation (SAAVE) estimates angular velocities when gyroscope measurements become saturated during aggressive motions, reducing angular speed estimation error by 83.4%. Second, Stretch-ICP, a novel registration and deskewing algorithm, enables reconstruction of smoother 6-Degrees Of Freedom (DOF) trajectories under aggressive motions compared to classical Iterative Closest Point (ICP). Stretch-ICP reduces linear and angular velocity errors by 95.2% and 94.8%, respectively, at scan boundaries. Together, these contributions improve the robustness and consistency of lidar-inertial state estimation under aggressive motions.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The manuscript introduces the publicly available TIGS dataset of lidar and IMU recordings from a mechanical rig tumbling down a hill, with angular speeds up to four times higher than prior datasets. It proposes SAAVE to estimate angular velocities during gyroscope saturation (83.4% error reduction) and Stretch-ICP, a continuous-trajectory registration and deskewing algorithm extending classical ICP, which reconstructs smoother 6-DOF trajectories and reduces linear/angular velocity errors by 95.2%/94.8% at scan boundaries on TIGS. The methods are positioned to improve lidar-inertial SLAM robustness under aggressive motions induced by loss of stability.

Significance. If the quantitative claims are reproducible and the rig dynamics prove representative, the work supplies a challenging new public benchmark and practical algorithmic tools that could meaningfully advance robust state estimation for robots on uneven or slippery terrain. The large reported error reductions and the continuous-trajectory formulation are concrete strengths that address a real gap in handling sensor corruption during extreme accelerations.

major comments (1)
  1. Evaluation section: the 95.2% linear and 94.8% angular velocity error reductions for Stretch-ICP, as well as the broader claim of improved 6-DOF trajectory reconstruction under aggressive motions, rest exclusively on the mechanical tumbling rig in TIGS. The manuscript does not report experiments on real robotic platforms (e.g., wheeled or legged robots with wheel slip or terrain-induced accelerations), so the translation from rig dynamics to the motivating robotic autonomy scenarios remains unverified and load-bearing for the central contribution.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their positive evaluation of the work's significance and for the constructive feedback on the evaluation. We address the major comment below.

read point-by-point responses

  1. Referee: [—] Evaluation section: the 95.2% linear and 94.8% angular velocity error reductions for Stretch-ICP, as well as the broader claim of improved 6-DOF trajectory reconstruction under aggressive motions, rest exclusively on the mechanical tumbling rig in TIGS. The manuscript does not report experiments on real robotic platforms (e.g., wheeled or legged robots with wheel slip or terrain-induced accelerations), so the translation from rig dynamics to the motivating robotic autonomy scenarios remains unverified and load-bearing for the central contribution.

    Authors: We thank the referee for this observation. The TIGS dataset was deliberately collected on a mechanical rig to produce repeatable, high-magnitude tumbling with angular rates up to four times those in prior datasets while avoiding the safety and repeatability issues of inducing equivalent motions on physical robots. This setup isolates the exact sensor corruptions (gyroscope saturation and motion distortion) that motivate the paper. We acknowledge that the rig does not reproduce every nuance of wheel slip or terrain interaction on wheeled or legged platforms. In the revised manuscript we have added a dedicated paragraph in the discussion section that explicitly relates the rig's acceleration profiles to those reported in the robotics literature for loss-of-stability events, thereby clarifying the link to the motivating autonomy scenarios. We view this as a partial but substantive response to the concern. revision: partial

Circularity Check

0 steps flagged

No circularity: Stretch-ICP and TIGS evaluation are self-contained

full rationale

The paper introduces the TIGS dataset and presents Stretch-ICP as a new continuous-trajectory registration algorithm. Reported performance figures (95.2% linear and 94.8% angular velocity error reduction at scan boundaries) are direct empirical measurements obtained by running the algorithm on the introduced dataset; they are not quantities fitted to a subset and then relabeled as predictions, nor do they reduce by construction to any self-citation or definitional loop. No load-bearing self-citations, uniqueness theorems imported from prior author work, or ansatz smuggling appear in the derivation chain. The algorithmic description and experimental protocol remain independent of the target metrics, satisfying the criteria for a non-circular contribution.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no explicit free parameters, axioms, or invented entities; the central claims rest on the new dataset and algorithmic descriptions whose internal details are not provided.

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