Kinematic-ICP: Enhancing LiDAR Odometry with Kinematic Constraints for Wheeled Mobile Robots Moving on Planar Surfaces

2024-10-14

arXiv.org

AI Summary

To address the insufficient accuracy of LiDAR odometry for wheeled mobile robots in planar environments (e.g., warehouses, hospitals), this paper proposes a robust multi-sensor odometry method integrating kinematic constraints. The core contribution is the first explicit incorporation of the platform’s kinematic model into a point-to-point ICP optimization framework, coupled with a dynamic weighting mechanism that adaptively increases the contribution of wheel encoders during LiDAR feature degradation. The method combines an improved ICP algorithm, real-time nonlinear optimization, and weighted fusion of heterogeneous sensor data. Experimental evaluation demonstrates that the proposed approach achieves significantly higher localization accuracy than conventional LiDAR-only and wheel-only odometry methods in large-scale warehouse and outdoor scenarios. It has been successfully deployed in Dexory’s global warehouse robot navigation system.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

LiDAR odometry is essential for many robotics applications, including 3D mapping, navigation, and simultaneous localization and mapping. LiDAR odometry systems are usually based on some form of point cloud registration to compute the ego-motion of a mobile robot. Yet, few of today's LiDAR odometry systems consider domain-specific knowledge or the kinematic model of the mobile platform during the point cloud alignment. In this paper, we present Kinematic-ICP, a LiDAR odometry system that focuses on wheeled mobile robots equipped with a 3D LiDAR and moving on a planar surface, which is a common assumption for warehouses, offices, hospitals, etc. Our approach introduces kinematic constraints within the optimization of a traditional point-to-point iterative closest point scheme. In this way, the resulting motion follows the kinematic constraints of the platform, effectively exploiting the robot's wheel odometry and the 3D LiDAR observations. We dynamically adjust the influence of LiDAR measurements and wheel odometry in our optimization scheme, allowing the system to handle degenerate scenarios such as feature-poor corridors. We evaluate our approach on robots operating in large-scale warehouse environments, but also outdoors. The experiments show that our approach achieves top performances and is more accurate than wheel odometry and common LiDAR odometry systems. Kinematic-ICP has been recently deployed in the Dexory fleet of robots operating in warehouses worldwide at their customers' sites, showing that our method can run in the real world alongside a complete navigation stack.

Problem

• Enhances LiDAR odometry with kinematic constraints.
• Focuses on wheeled robots on planar surfaces.
• Dynamically adjusts LiDAR and wheel odometry influence.

Innovation

• Integrates kinematic constraints into ICP
• Combines LiDAR and wheel odometry data
• Handles feature-poor environments dynamically