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

• URL: http://arxiv.org/abs/2206.15154v1
• Date: Thu, 30 Jun 2022 09:39:08 GMT
• Title: BoxGraph: Semantic Place Recognition and Pose Estimation from 3D LiDAR
• Authors: Georgi Pramatarov, Daniele De Martini, Matthew Gadd, Paul Newman
• Abstract summary: We model 3D point clouds as fully-connected graphs of semantically identified components. Optimal association across graphs allows for full 6-Degree-of-Freedom (DoF) pose estimation and place recognition. This representation is very concise, condensing the size of maps by a factor of 25 against the state-of-the-art.
• Score: 22.553026961366005
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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