Fast Sparse LiDAR Odometry Using Self-Supervised Feature Selection on Intensity Images

Ego-motion estimation is a fundamental building block of any autonomous system that needs to navigate in an environment. In large-scale outdoor scenes, 3D LiDARs are often used for this task, as they provide a large number of range measurements at high precision. In this paper, we propose a novel approach that exploits the intensity channel of 3D LiDAR scans to compute an accurate odometry estimate at a high frequency. In contrast to existing methods that operate on full point clouds, our approach extracts a sparse set of salient points from intensity images using data-driven feature extraction architectures originally designed for RGB images. These salient points are then used to compute the relative pose between successive scans. Furthermore, we propose a novel self-supervised procedure to fine-tune the feature extraction network online during navigation, which exploits the estimated relative motion but does not require ground truth data. The experimental evaluation suggests that the proposed approach provides a solid ego-motion estimation at a much higher frequency than the sensor frame rate while improving its estimation accuracy online.