MICP-L: Mesh-based ICP for Robot Localization using Hardware-Accelerated Ray Casting

TL;DR

This paper introduces MICP-L, a fast, mesh-based ICP method for robot localization that leverages hardware acceleration for real-time performance in GPS-denied environments.

Contribution

The paper presents a novel, efficient mesh-based ICP localization method that utilizes hardware acceleration and parallel computation for real-time robot localization.

Findings

Robust localization across agricultural, aerial, and automotive datasets.

Supports various hardware platforms including CPUs, GPUs, and NVIDIA RTX.

Achieves real-time performance with optimized ray casting and covariance computation.

Abstract

Triangle mesh maps are a versatile 3D environment representation for robots to navigate in challenging indoor and outdoor environments exhibiting tunnels, hills and varying slopes. To make use of these mesh maps, methods are needed to accurately localize robots in such maps to perform essential tasks like path planning and navigation. We present Mesh ICP Localization (MICP-L), a novel and computationally efficient method for registering one or more range sensors to a triangle mesh map to continuously localize a robot in 6D, even in GPS-denied environments. We accelerate the computation of ray casting correspondences (RCC) between range sensors and mesh maps by supporting different parallel computing devices like multicore CPUs, GPUs and the latest NVIDIA RTX hardware. By additionally transforming the covariance computation into a reduction operation, we can optimize the initial guessed poses in parallel on CPUs or GPUs, making our implementation applicable in real-time on many architectures. We demonstrate the robustness of our localization approach with datasets from agricultural, aerial, and automotive domains.