Self-supervised Learning of LiDAR Odometry for Robotic Applications

TL;DR

This paper introduces a versatile self-supervised LiDAR odometry method that enables real-time robot pose estimation without requiring ground-truth data, adaptable across various environments and robot types.

Contribution

It presents a novel self-supervised learning approach for LiDAR odometry that is environment-agnostic and does not need labeled data, suitable for diverse robotic applications.

Findings

Effective in indoor and outdoor environments

No ground-truth data required for training

Compatible with various robot platforms

Abstract

Reliable robot pose estimation is a key building block of many robot autonomy pipelines, with LiDAR localization being an active research domain. In this work, a versatile self-supervised LiDAR odometry estimation method is presented, in order to enable the efficient utilization of all available LiDAR data while maintaining real-time performance. The proposed approach selectively applies geometric losses during training, being cognizant of the amount of information that can be extracted from scan points. In addition, no labeled or ground-truth data is required, hence making the presented approach suitable for pose estimation in applications where accurate ground-truth is difficult to obtain. Furthermore, the presented network architecture is applicable to a wide range of environments and sensor modalities without requiring any network or loss function adjustments. The proposed approach is thoroughly tested for both indoor and outdoor real-world applications through a variety of experiments using legged, tracked and wheeled robots, demonstrating the suitability of learning-based LiDAR odometry for complex robotic applications.