RH-Map: Online Map Construction Framework of Dynamic Objects Removal Based on Region-wise Hash Map Structure

TL;DR

This paper introduces RH-Map, a real-time map construction framework that effectively removes dynamic objects using a region-wise hash map, enhancing outdoor robot navigation accuracy.

Contribution

The paper presents a novel 3D region-wise hash map structure and a combined front-end and back-end approach for online dynamic object removal in map construction.

Findings

Effective real-time dynamic object removal demonstrated on SemanticKITTI

Improved map accuracy in outdoor environments

Validated performance in real-world robot navigation scenarios

Abstract

Mobile robots navigating in outdoor environments frequently encounter the issue of undesired traces left by dynamic objects and manifested as obstacles on map, impeding robots from achieving accurate localization and effective navigation. To tackle the problem, a novel map construction framework based on 3D region-wise hash map structure (RH-Map) is proposed, consisting of front-end scan fresher and back-end removal modules, which realizes real-time map construction and online dynamic object removal (DOR). First, a two-layer 3D region-wise hash map structure of map management is proposed for effective online DOR. Then, in scan fresher, region-wise ground plane estimation (R-GPE) is adopted for estimating and preserving ground information and Scan-to-Map Removal (S2M-R) is proposed to discriminate and remove dynamic regions. Moreover, the lightweight back-end removal module maintaining keyframes is proposed for further DOR. As experimentally verified on SemanticKITTI, our proposed framework yields promising performance on online DOR of map construction compared with the state-of-the-art methods. And we also validate the proposed framework in real-world environments.