A SLAM Method for Indoor Degradation Environment Based on Lidar/IMU/UWB Fusion

Currently, 3D Lidar is widely used in indoor SLAM tasks due to its stable and reliable measurement performance. However, it suffers from significant positioning and mapping errors in indoor degraded environments, making it difficult to ensure high global consistency. This issue has long constrained its development. To address these challenges, this paper proposes an indoor mobile robot SLAM algorithm based on multi-sensor information fusion. This method bypasses the need for relying solely on degraded state estimation, thereby overcoming the limitations of a single sensor in real-world environments by leveraging complementary features between sensors to solve the indoor degradation problem. This paper integrates Inertial Measurement Unit (IMU), Ultra-Wideband (UWB) ranging, and Lidar information, and designs a complete SLAM algorithm framework based on nonlinear optimization and an error-state Kalman filter. At the same time, the degradation level of the point cloud perceived by the Lidar is used as a key basis for selecting the UWB residual weight, thereby addressing the problem of indoor Lidar point cloud degradation. Finally, this paper demonstrates the superiority of the algorithm through real-world experiments.