Finite-time Stable Pose Estimation on TSE(3) using Point Cloud and Velocity Sensors

• URL: http://arxiv.org/abs/2602.09414v1
• Date: Tue, 10 Feb 2026 05:11:01 GMT
• Title: Finite-time Stable Pose Estimation on TSE(3) using Point Cloud and Velocity Sensors
• Abstract summary: This work presents a finite-time stable pose estimator (FTS-PE) for rigid bodies undergoing rotational and translational motion in three dimensions.<n>The FTS-PE is a full-state observer for the pose (position and orientation) and velocities and is obtained through a Lyapunov analysis.<n> numerical simulations compare the FTS-PE with a dual-quaternion extended Kalman filter and our previously developed variational pose estimator (VPE)
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This work presents a finite-time stable pose estimator (FTS-PE) for rigid bodies undergoing rotational and translational motion in three dimensions, using measurements from onboard sensors that provide position vectors to inertially-fixed points and body velocities. The FTS-PE is a full-state observer for the pose (position and orientation) and velocities and is obtained through a Lyapunov analysis that shows its stability in finite time and its robustness to bounded measurement noise. Further, this observer is designed directly on the state space, the tangent bundle of the Lie group of rigid body motions, SE(3), without using local coordinates or (dual) quaternion representations. Therefore, it can estimate arbitrary rigid body motions without encountering singularities or the unwinding phenomenon and be readily applied to autonomous vehicles. A version of this observer that does not need translational velocity measurements and uses only point clouds and angular velocity measurements from rate gyros, is also obtained. It is discretized using the framework of geometric mechanics for numerical and experimental implementations. The numerical simulations compare the FTS-PE with a dual-quaternion extended Kalman filter and our previously developed variational pose estimator (VPE). The experimental results are obtained using point cloud images and rate gyro measurements obtained from a Zed 2i stereo depth camera sensor. These results validate the stability and robustness of the FTS-PE.

Related papers

• Event-Based Motion Estimation via Oriented Distance Fields [55.23416502666468]
  Event-based motion estimation is central to tasks that demand high temporal resolution and robustness to fast motion.<n>We propose Oriented Distance Field Motion Estimation (ODF Motion Estimation), which replaces this optimization with a single averaging step over a precomputed field of event distance vectors.<n>On public and self-collected datasets, ODF motion estimation reaches sub-pixel accuracy at the lowest latency among compared methods.
  arXiv  Detail & Related papers  (2026-08-25T08:27:42Z)
• Attitude Estimation Using Inertial and Barometric Measurements [0.0]
  This paper introduces a barometer-aided attitude estimation architecture that exploits barometric altitude measurements to provide complementary information on the vehicle's vertical motion.<n>Results demonstrate that barometer-aided estimation provides a lightweight, reliable, and effective complementary sensing modality for attitude estimation in minimal-sensing configurations.
  arXiv  Detail & Related papers  (2026-07-14T20:36:13Z)
• Stereo-Inertial Poser: Towards Metric-Accurate Shape-Aware Motion Capture Using Sparse IMUs and a Single Stereo Camera [54.967647497048205]
  We present Stereo-Inertial Poser, a real-time motion capture system that estimates metric-accurate and shape-aware 3D human motion.<n>We replace the monocular RGB with stereo vision, enabling direct 3D keypoint extraction and body shape parameter estimation.<n>Our method produces drift-free global translation under a long recording time and reduces foot-skating effects.
  arXiv  Detail & Related papers  (2026-03-02T17:46:38Z)
• Vision-Aided Relative State Estimation for Approach and Landing on a Moving Platform with Inertial Measurements [36.94429692322632]
  This paper tackles the problem of estimating the relative position, orientation, and velocity between a UAV and a planar platform undergoing arbitrary 3D motion during approach and landing.<n>We propose a cascade observer with a complementary filter on SO(3) to reconstruct the relative attitude, followed by a linear Riccati observer for relative position and velocity estimation.<n>The performance of the proposed observers is validated through extensive simulations.
  arXiv  Detail & Related papers  (2025-12-22T10:28:20Z)
• Cascaded Tightly-Coupled Observer Design for Single-Range-Aided Inertial Navigation [0.0]
  This work introduces a single-range-aided navigation observer that reconstructs the full state of a rigid body using only an Inertial Measurement Unit (IMU)<n>The design first formulates an extended linear time-varying (LTV) system to estimate body-frame position, body-frame velocity, and the gravity direction.<n>Almost Global Asymptotic Stability (AGAS) of the cascaded design is established under a uniform observability condition, ensuring robustness to sensor noise and trajectory variations.
  arXiv  Detail & Related papers  (2025-12-05T22:44:22Z)
• Continuous-Time SO(3) Forecasting with Savitzky--Golay Neural Controlled Differential Equations [51.510040541600176]
  This work proposes modeling continuous-time rotational object dynamics on $SO(3)$.<n>Unlike existing methods that rely on simplified motion assumptions, our method learns a general latent dynamical system of the underlying object trajectory.<n> Experimental results on real-world data demonstrate compelling forecasting capabilities compared to existing approaches.
  arXiv  Detail & Related papers  (2025-06-07T12:41:50Z)
• EMoTive: Event-guided Trajectory Modeling for 3D Motion Estimation [59.33052312107478]
  Event cameras offer possibilities for 3D motion estimation through continuous adaptive pixel-level responses to scene changes.<n>This paper presents EMove, a novel event-based framework that models-uniform trajectories via event-guided parametric curves.<n>For motion representation, we introduce a density-aware adaptation mechanism to fuse spatial and temporal features under event guidance.<n>The final 3D motion estimation is achieved through multi-temporal sampling of parametric trajectories, flows and depth motion fields.
  arXiv  Detail & Related papers  (2025-03-14T13:15:54Z)
• Sensitivity and Bandwidth of a Point-Source-Interferometry-based Inertial Measurement Unit Employing Large Momentum Transfer and Launched Atoms [2.196609464753099]
  We analyze theoretically the sensitivity of accelerometry and rotation sensing with a point source interferometer employing large momentum transfer (LMT) We present a design of an inertial measurement unit (IMU) that can measure rotation around and acceleration along each of the three axes.
  arXiv  Detail & Related papers  (2024-02-06T00:52:24Z)
• Tight Fusion of Events and Inertial Measurements for Direct Velocity Estimation [20.002238735553792]
  We propose a novel solution to tight visual-inertial fusion directly at the level of first-order kinematics by employing a dynamic vision sensor instead of a normal camera. We demonstrate how velocity estimates in highly dynamic situations can be obtained over short time intervals. Experiments on both simulated and real data demonstrate that the proposed tight event-inertial fusion leads to continuous and reliable velocity estimation.
  arXiv  Detail & Related papers  (2024-01-17T15:56:57Z)
• One-Shot Initial Orbit Determination in Low-Earth Orbit [0.49157446832511503]
  State-of-the-art methodologies for initial orbit determination consist of Kalman-type filters. We formulate the problem of initial orbit determination as a Weighted Least Squares. We numerically demonstrate that our estimator is able to attain better accuracy on the state estimation than the trilateration method.
  arXiv  Detail & Related papers  (2023-12-20T11:49:38Z)
• Averaging Spatio-temporal Signals using Optimal Transport and Soft Alignments [110.79706180350507]
  We show that our proposed loss can be used to define temporal-temporal baryechecenters as Fr'teche means duality. Experiments on handwritten letters and brain imaging data confirm our theoretical findings.
  arXiv  Detail & Related papers  (2022-03-11T09:46:22Z)
• TLIO: Tight Learned Inertial Odometry [43.17991168599939]
  We propose a tightly-coupled Extended Kalman Filter framework for IMU-only state estimation. We show that our network, trained with pedestrian data from a headset, can produce statistically consistent measurement and uncertainty.
  arXiv  Detail & Related papers  (2020-07-06T03:13:34Z)
• Online Initialization and Extrinsic Spatial-Temporal Calibration for Monocular Visual-Inertial Odometry [19.955414423860788]
  This paper presents an online method for bootstrapping the optimization-based monocular visual-inertial odometry (VIO) The method can online calibrate the relative transformation (spatial) and time offsets (temporal) among camera and IMU, as well as estimate the initial values of metric scale, velocity, gravity, gyroscope bias, and accelerometer bias. Experimental results on public datasets show that the initial values and the parameters, as well as the sensor poses, can be accurately estimated by the proposed method.
  arXiv  Detail & Related papers  (2020-04-12T03:13:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.