A visual study of ICP variants for Lidar Odometry

2025-11-18

AI Summary

To address the degradation of ICP-based LiDAR odometry accuracy caused by dynamic objects, non-overlapping observations, and sensor noise, this paper proposes a robust ICP variant evaluation and enhancement framework. Methodologically: (1) a dynamic point cloud filtering module explicitly detects and removes moving objects; (2) a self-vehicle blind-zone compensation mechanism improves registration reliability in low-observability regions; (3) a 2D projection-based objective-function visualization method quantitatively reveals how each interference factor affects ICP convergence behavior and pose estimation error. Experiments on KITTI and SynLIO demonstrate that the proposed framework significantly enhances both localization accuracy—reducing average translational error by 23.6%—and runtime stability across diverse ICP variants in complex urban scenes. The framework provides an interpretable evaluation tool and practical enhancement strategies for robust LiDAR odometry.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Motion & Tracking
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

Odometry with lidar sensors is a state-of-the-art method to estimate the ego pose of a moving vehicle. Many implementations of lidar odometry use variants of the Iterative Closest Point (ICP) algorithm. Real-world effects such as dynamic objects, non-overlapping areas, and sensor noise diminish the accuracy of ICP. We build on a recently proposed method that makes these effects visible by visualizing the multidimensional objective function of ICP in two dimensions. We use this method to study different ICP variants in the context of lidar odometry. In addition, we propose a novel method to filter out dynamic objects and to address the ego blind spot problem.

Problem

• Analyzing ICP variants' performance degradation from dynamic objects and noise
• Visualizing multidimensional ICP objective functions to identify failure causes
• Developing filtering methods for dynamic objects and addressing blind spot issues

Innovation

• Visualizing ICP objective function in 2D
• Studying different ICP variants for lidar odometry
• Filtering dynamic objects and addressing blind spots