Robust Direct Multi-Camera SLAM in Challenging Scenarios

Traditional monocular and stereo visual SLAM systems often fail to operate stably in complex unstructured environments (e.g., weakly textured or repetitively textured scenes) due to feature scarcity from their limited fields of view. In contrast, multi-camera systems can effectively overcome the perceptual limitations of monocular or stereo setups by providing broader field-of-view coverage. However, most existing multi-camera visual SLAM systems are primarily feature-based and thus still constrained by the inherent limitations of feature extraction in such environments. To address this issue, a multi-camera visual SLAM framework based on the direct method is proposed. In the front-end, a detector-free matcher named Efficient LoFTR is incorporated, enabling pose estimation through dense pixel associations to improve localization accuracy and robustness. In the back-end, geometric constraints among multiple cameras are integrated, and system localization accuracy is further improved through a joint optimization process. Through extensive experiments on public datasets and a self-built simulation dataset, the proposed method achieves superior performance over state-of-the-art approaches regarding localization accuracy, trajectory completeness, and environmental adaptability, thereby validating its high robustness in complex unstructured environments.