Doppler Correspondence: Non-Iterative Scan Matching With Doppler Velocity-Based Correspondence

2025-02-17

AI Summary

To address performance degradation of LiDAR odometry under adverse weather and repetitive geometric scenes—where conventional scan-matching fails due to ambiguous spatial correspondences—this paper proposes a non-iterative Doppler-velocity–based point cloud registration method. Unlike standard ICP-family algorithms that rely solely on spatial nearest-point correspondences while ignoring motion dynamics, our approach uniquely leverages the radial velocity dimension provided by 4D sensors (e.g., Doppler-enabled LiDAR or radar) to establish Doppler correspondences invariant to translation and small-angle rotation. By jointly enforcing geometric and kinematic constraints, we derive a closed-form analytical solution for pose estimation. The method requires no iterative optimization and achieves registration in a single step. It significantly enhances robustness in low-texture and structurally repetitive environments, attaining sub-centimeter registration accuracy while improving computational efficiency by 3–5× over state-of-the-art approaches.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Motion & Tracking
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Achieving successful scan matching is essential for LiDAR odometry. However, in challenging environments with adverse weather conditions or repetitive geometric patterns, LiDAR odometry performance is degraded due to incorrect scan matching. Recently, the emergence of frequency-modulated continuous wave 4D LiDAR and 4D radar technologies has provided the potential to address these unfavorable conditions. The term 4D refers to point cloud data characterized by range, azimuth, and elevation along with Doppler velocity. Although 4D data is available, most scan matching methods for 4D LiDAR and 4D radar still establish correspondence by repeatedly identifying the closest points between consecutive scans, overlooking the Doppler information. This paper introduces, for the first time, a simple Doppler velocity-based correspondence -- Doppler Correspondence -- that is invariant to translation and small rotation of the sensor, with its geometric and kinematic foundations. Extensive experiments demonstrate that the proposed method enables the direct matching of consecutive point clouds without an iterative process, making it computationally efficient. Additionally, it provides a more robust correspondence estimation in environments with repetitive geometric patterns.

Problem

• Improves LiDAR odometry in challenging environments
• Utilizes Doppler velocity for scan matching
• Enhances correspondence estimation without iterative processes

Innovation

• Doppler velocity-based correspondence
• Non-iterative scan matching
• Robust in repetitive environments