BoxGraph: Semantic Place Recognition and Pose Estimation from 3D LiDAR

TL;DR

BoxGraph introduces a lightweight, graph-based method for robust place recognition and pose estimation from 3D LiDAR data, significantly reducing map size and achieving state-of-the-art accuracy on SemanticKITTI.

Contribution

The paper presents a novel graph-based representation of 3D LiDAR point clouds for place recognition and pose estimation, offering a highly concise map format and improved accuracy.

Findings

Achieves 88.4% recall at 100% precision in place recognition.

Condenses map size by a factor of 25 compared to state-of-the-art.

Median pose errors of 10 cm and 0.33 degrees.

Abstract

This paper is about extremely robust and lightweight localisation using LiDAR point clouds based on instance segmentation and graph matching. We model 3D point clouds as fully-connected graphs of semantically identified components where each vertex corresponds to an object instance and encodes its shape. Optimal vertex association across graphs allows for full 6-Degree-of-Freedom (DoF) pose estimation and place recognition by measuring similarity. This representation is very concise, condensing the size of maps by a factor of 25 against the state-of-the-art, requiring only 3kB to represent a 1.4MB laser scan. We verify the efficacy of our system on the SemanticKITTI dataset, where we achieve a new state-of-the-art in place recognition, with an average of 88.4% recall at 100% precision where the next closest competitor follows with 64.9%. We also show accurate metric pose estimation performance - estimating 6-DoF pose with median errors of 10 cm and 0.33 deg.