Degeneracy‐Resistant LiDAR‐SLAM Algorithm Based on Geometric and Visual Features' Fusion

Abstract

ABSTRACT Simultaneous localization and mapping (SLAM) is at the core of robotics automation, relying on sensors such as Laser Detection and Ranging (LiDAR) and cameras to digitally construct a robot's environment and determine its position within. LiDAR‐based SLAM outperforms visual‐SLAM, especially in low visibility and challenging lighting conditions. However, these systems still face challenges like scene degradation when dealing with feature‐deficient degenerate environments such as long corridors or tunnels. Traditional LiDAR SLAM algorithms primarily focus on the extraction of geometric features from the scene, with less utilization of visual information, for example, LiDAR‐generated reflectivity (also commonly referred to as intensity image) and depth imagery. In this study, we explore the potential of fusing both geometric and LiDAR‐generated image features into the SLAM system in various forms, aiming to enhance the system's adaptability in diverse environments and its robustness against environment degeneracy. We propose a new multifeature‐modality SLAM designed for robust real‐time localization and mapping in challenging environments. Our method enhances and extracts visual features from LiDAR‐generated images, which are then fused with geometric features through a holistic residual function for pose optimization. We also integrate a deep learning‐based object removal algorithm to reduce sensitivity to moving objects and sensor noise. This article conducts an in‐depth comparison of the proposed algorithm with several leading technologies in terms of scan matching accuracy, robustness, odometry, and mapping. The experimental results vividly showcase the superiority of our method in achieving high scan matching success rates and strong resilience against random outliers and Gaussian noise across various challenging scenarios, compared to the existing LiDAR SLAM methods that rely solely on geometric features. Extensive field experiments conducted on publicly available data sets, along with independently developed backpack‐based and robotic platforms, validated the robustness and accuracy of the proposed approach in both indoor and outdoor environments. In 3D mapping, we quantified the precision of 3D points by comparing point clouds collected by high‐precision Mobile Laser Scanning (MLS) and Terrestrial Laser Scanning (TLS). Our method outperforms in terms of absolute pose errors (APE) and point cloud matching quality. Based on the fitted Weibull distribution, the root mean square error (RMS) of point‐to‐plane distances improved by 20%. Additionally, ablation tests revealed the efficacy of different components within our system.