Real-time Multi-Adaptive-Resolution-Surfel 6D LiDAR Odometry using Continuous-time Trajectory Optimization

TL;DR

This paper introduces a real-time 6D LiDAR odometry method that uses continuous-time trajectory optimization and adaptive multi-resolution surfel maps to improve SLAM performance for autonomous robots.

Contribution

It combines continuous-time B-Spline trajectories with GMM-based map alignment and adaptive resolution selection for efficient real-time LiDAR odometry.

Findings

Effective on multiple datasets

Real-robot experiments demonstrate robustness

Speeds up registration process

Abstract

Simultaneous Localization and Mapping (SLAM) is an essential capability for autonomous robots, but due to high data rates of 3D LiDARs real-time SLAM is challenging. We propose a real-time method for 6D LiDAR odometry. Our approach combines a continuous-time B-Spline trajectory representation with a Gaussian Mixture Model (GMM) formulation to jointly align local multi-resolution surfel maps. Sparse voxel grids and permutohedral lattices ensure fast access to map surfels, and an adaptive resolution selection scheme effectively speeds up registration. A thorough experimental evaluation shows the performance of our approach on multiple datasets and during real-robot experiments.