Online Semantic-Assisted Topological Map Building With LiDAR in Large-Scale Outdoor Environments: Toward Robust Place Recognition

Abstract

The building of map model is crucial to the efficiency of unmanned ground vehicles’ (UGVs) navigation and robust place recognition. This paper presents an online semantic-assisted topological map building approach using 3-D LiDAR point clouds in large-scale outdoor environments. To achieve global consistency while switching between global navigation satellite system (GNSS)-denied and GNSS-fixed scenes, a pose estimation module composed of semantic-assisted LiDAR-simultaneous localization and mapping (SLAM) and the GNSS positioning is designed and integrated seamlessly, which can supply both the UGV’s global 6-degrees of freedom (DoF) poses and the local semantic-metric map. On the basis of semantic-metric map output, an online topological map building framework is implemented aiming to construct the topological map with robust place recognition performance, in which a novel local node density adjustment (LoNDA) algorithm is proposed to online adjust the distance between adjacent nodes based on both the targeted node density and appearance similarity. Field experiments are conducted on both self-collected datasets and public datasets. Experimental results demonstrate that the proposed approach can perform the online LiDAR-based topological map building with high accuracy, and the proposed LoNDA algorithm can achieve superior place recognition performance than the density-based scheme.