DICP: Doppler Iterative Closest Point Algorithm

• URL: http://arxiv.org/abs/2201.11944v1
• Date: Fri, 28 Jan 2022 05:51:07 GMT
• Title: DICP: Doppler Iterative Closest Point Algorithm
• Authors: Bruno Hexsel, Heethesh Vhavle and Yi Chen
• Abstract summary: We present a novel algorithm for point cloud registration for range sensors capable of measuring per-return instantaneous radial velocity: Doppler ICP. We propose a new Doppler velocity objective function that exploits the compatibility of each point's Doppler measurement and the sensor's current motion estimate. Our results show a significant performance improvement in terms of the registration accuracy with the added benefit of faster convergence guided by the Doppler velocity gradients.
• Score: 5.934931737701265
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we present a novel algorithm for point cloud registration for range sensors capable of measuring per-return instantaneous radial velocity: Doppler ICP. Existing variants of ICP that solely rely on geometry or other features generally fail to estimate the motion of the sensor correctly in scenarios that have non-distinctive features and/or repetitive geometric structures such as hallways, tunnels, highways, and bridges. We propose a new Doppler velocity objective function that exploits the compatibility of each point's Doppler measurement and the sensor's current motion estimate. We jointly optimize the Doppler velocity objective function and the geometric objective function which sufficiently constrains the point cloud alignment problem even in feature-denied environments. Furthermore, the correspondence matches used for the alignment are improved by pruning away the points from dynamic targets which generally degrade the ICP solution. We evaluate our method on data collected from real sensors and from simulation. Our results show a significant performance improvement in terms of the registration accuracy with the added benefit of faster convergence guided by the Doppler velocity gradients.

Related papers

• Universal Online Temporal Calibration for Optimization-based Visual-Inertial Navigation Systems [13.416013522770905]
  We propose a universal online temporal calibration strategy for optimization-based visual-inertial navigation systems. We use the time offset td as a state parameter in the optimization residual model to align the IMU state to the corresponding image timestamp. Our approach provides more accurate time offset estimation and faster convergence, particularly in the presence of noisy sensor data.
  arXiv  Detail & Related papers  (2025-01-03T12:41:25Z)
• Event-Based Tracking Any Point with Motion-Augmented Temporal Consistency [58.719310295870024]
  This paper presents an event-based framework for tracking any point. It tackles the challenges posed by spatial sparsity and motion sensitivity in events. It achieves 150% faster processing with competitive model parameters.
  arXiv  Detail & Related papers  (2024-12-02T09:13:29Z)
• SPARE: Symmetrized Point-to-Plane Distance for Robust Non-Rigid Registration [76.40993825836222]
  We propose SPARE, a novel formulation that utilizes a symmetrized point-to-plane distance for robust non-rigid registration. The proposed method greatly improves the accuracy of non-rigid registration problems and maintains relatively high solution efficiency.
  arXiv  Detail & Related papers  (2024-05-30T15:55:04Z)
• Vanishing Point Estimation in Uncalibrated Images with Prior Gravity Direction [82.72686460985297]
  We tackle the problem of estimating a Manhattan frame. We derive two new 2-line solvers, one of which does not suffer from singularities affecting existing solvers. We also design a new non-minimal method, running on an arbitrary number of lines, to boost the performance in local optimization.
  arXiv  Detail & Related papers  (2023-08-21T13:03:25Z)
• GraphReg: Dynamical Point Cloud Registration with Geometry-aware Graph Signal Processing [0.0]
  This study presents a high-accuracy, efficient, and physically induced method for 3D point cloud registration. We explore geometry aware rigid-body dynamics to regulate the particle (point) motion, which results in more precise and robust registration. Results demonstrate that our proposed method outperforms state-of-the-art approaches in terms of accuracy and is more suitable for registering large-scale point clouds.
  arXiv  Detail & Related papers  (2023-02-02T14:06:46Z)
• Correlating sparse sensing for large-scale traffic speed estimation: A Laplacian-enhanced low-rank tensor kriging approach [76.45949280328838]
  We propose a Laplacian enhanced low-rank tensor (LETC) framework featuring both lowrankness and multi-temporal correlations for large-scale traffic speed kriging. We then design an efficient solution algorithm via several effective numeric techniques to scale up the proposed model to network-wide kriging.
  arXiv  Detail & Related papers  (2022-10-21T07:25:57Z)
• Globally Optimal Event-Based Divergence Estimation for Ventral Landing [55.29096494880328]
  Event sensing is a major component in bio-inspired flight guidance and control systems. We explore the usage of event cameras for predicting time-to-contact with the surface during ventral landing. This is achieved by estimating divergence (inverse TTC), which is the rate of radial optic flow, from the event stream generated during landing. Our core contributions are a novel contrast maximisation formulation for event-based divergence estimation, and a branch-and-bound algorithm to exactly maximise contrast and find the optimal divergence value.
  arXiv  Detail & Related papers  (2022-09-27T06:00:52Z)
• StreamYOLO: Real-time Object Detection for Streaming Perception [84.2559631820007]
  We endow the models with the capacity of predicting the future, significantly improving the results for streaming perception. We consider multiple velocities driving scene and propose Velocity-awared streaming AP (VsAP) to jointly evaluate the accuracy. Our simple method achieves the state-of-the-art performance on Argoverse-HD dataset and improves the sAP and VsAP by 4.7% and 8.2% respectively.
  arXiv  Detail & Related papers  (2022-07-21T12:03:02Z)
• LiDAR Point--to--point Correspondences for Rigorous Registration of Kinematic Scanning in Dynamic Networks [0.0]
  We propose a novel trajectory adjustment procedure to improve the registration of LiDAR point clouds. We describe the method for selecting correspondences and how they are inserted into the Dynamic Network as new observation models. We then describe the experiments conducted to evaluate the performance of the proposed framework in practical airborne laser scanning scenarios with low-cost MEMS inertial sensors.
  arXiv  Detail & Related papers  (2022-01-03T11:53:55Z)
• Efficient Real-Time Radial Distortion Correction for UAVs [1.7149364927872015]
  We present a novel algorithm for onboard radial distortion correction for unmanned aerial vehicles (UAVs) equipped with an inertial measurement unit (IMU) This approach makes calibration procedures redundant, thus allowing for exchange of optics extemporaneously. We propose a fast and robust minimal solver for simultaneously estimating the focal length, radial distortion profile and motion parameters from homographies.
  arXiv  Detail & Related papers  (2020-10-08T18:34:56Z)
• Real-Time Point Cloud Fusion of Multi-LiDAR Infrastructure Sensor Setups with Unknown Spatial Location and Orientation [0.0]
  We present an algorithm that is completely detached from external assistance and runs fully automatically. Our method focuses on the high-precision fusion of LiDAR point clouds. Experiments in simulation as well as with real measurements have shown that our algorithm performs a continuous point cloud registration of up to four 64-layer LiDARs in real-time.
  arXiv  Detail & Related papers  (2020-07-28T08:43:39Z)

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.