Towards 6D MCL for LiDARs in 3D TSDF Maps on Embedded Systems with GPUs

TL;DR

This paper presents a GPU-accelerated Monte Carlo Localization method for 3D LiDAR-based mapping in embedded systems, achieving real-time performance and high energy efficiency for 6D localization in robotics.

Contribution

It introduces a massively parallel GPU implementation of particle evaluation in 3D TSDF maps for LiDARs, enabling real-time 6D localization on embedded systems.

Findings

GPU implementation is 30x faster than CPU

Energy efficiency is over 50x better on GPU

Enables real-time 6D localization in embedded systems

Abstract

Monte Carlo Localization is a widely used approach in the field of mobile robotics. While this problem has been well studied in the 2D case, global localization in 3D maps with six degrees of freedom has so far been too computationally demanding. Hence, no mobile robot system has yet been presented in literature that is able to solve it in real-time. The computationally most intensive step is the evaluation of the sensor model, but it also offers high parallelization potential. This work investigates the massive parallelization of the evaluation of particles in truncated signed distance fields for three-dimensional laser scanners on embedded GPUs. The implementation on the GPU is 30 times as fast and more than 50 times more energy efficient compared to a CPU implementation.