SLAM-TSM: Enhanced Indoor LiDAR SLAM With Total Station Measurements for Accurate Trajectory Estimation

Simultaneous Localization and Mapping (SLAM) is a crucial task in various domains, including intelligent robotics, computer vision, and indoor navigation. Accurate and robust trajectory estimation is especially challenging in indoor environments due to the presence of feature-poor or repetitive scenes, limited visibility, and dynamic objects. Obtaining highly accurate machine platform odometry is also an important basis for solving the "last mile" problem in intelligent transportation. This paper proposes a novel algorithm that combines LiDAR-based SLAM, total station measurements, and graph optimization to optimize the robot's trajectory in indoor environments. By integrating highly accurate positional data from total station measurements as additional constraints, the proposed method enhances the performance of indoor LiDAR SLAM, effectively addressing the challenges of drift and trajectory offsets. Moreover, the proposed algorithm can provide trajectory optimization even in the absence of loop closure detection, making it more robust and suitable for a broader range of indoor environments. Experimental results validated the effectiveness of the proposed approach in reducing drift and improving trajectory estimation for low-cost indoor LiDAR devices, demonstrating its potential in various applications such as autonomous navigation, facility management, and augmented reality. It provides targeted ground truth for autonomous driving of machine platforms in intelligent transportation scenarios.