VPE-SLAM: Virtual Point Enhanced SLAM Using Solid-State LiDAR for Weak Feature Environments

The extraction of features from LiDAR point clouds is a pivotal aspect of simultaneous localization and mapping (SLAM). However, in environments with weak features, such as parking structures and long corridors, the efficacy of current feature extraction methods is notably reduced. To overcome this limitation, a SLAM algorithm aimed at enhancing feature extraction performance is proposed. This algorithm commences by extracting edge and plane features from the original point clouds. Then, the plane points are categorized into ground points, ground-similarity points, and non-ground points, based on structural characteristics of the environment. Building upon this, a plane-point-based edge feature prediction method is proposed to generate virtual edge points of the environment, and the predicted points are then combined with the edge points to form the enhanced final edge features. In the map construction phase, a two-step Levenberg-Marquardt (LM) optimization process, guided by a novel feature regrouping strategy, is utilized to achieve high precision mapping. The performance of the algorithm is empirically validated in long corridor settings using the Livox Horizon LiDAR system. Comparative evaluations with Livox Loam and Fast LIO2 illustrate that our algorithm significantly improves feature extraction and localization accuracy.