Simultaneous localization and mapping (SLAM) technology is ubiquitously employed in ground robots, unmanned aerial vehicles, and autonomous cars. This paper presents LTA-OM: an efficient, robust, and accurate LiDAR SLAM system. Employing FAST-LIO2 and Stable Triangle Descriptor as LiDAR-IMU odometry and the loop detection method, respectively, LTA-OM is implemented to be functionally complete, including loop detection and correction, false positive loop closure rejection, long-term association mapping, and multi-session localization and mapping. One novelty of this paper is the real-time long-term association (LTA) mapping, which exploits the direct scan-to-map registration of FAST-LIO2 and employs the corrected history map to constrain the mapping process globally. LTA leads to more globally consistent map construction and drift-less odometry at revisit places. We exhaustively benchmark LTA-OM and other state-of-the-art LiDAR systems with 18 data sequences. The results show that LTA-OM steadily outperforms other systems regarding trajectory accuracy, map consistency, and time consumption. The robustness of LTA-OM is validated in a challenging scene - a multi-level building having similar structures at different levels. Besides, a multi-session mode is designed to allow the user to store current session’s results, including the corrected map points, optimized odometry, and descriptor database for future sessions. The benefits of this mode are additional accuracy improvement and consistent map stitching, which is helpful for life-long mapping. Furthermore, LTA-OM has valuable features for robot control and path planning, including high-frequency and real-time odometry, drift-less odometry at revisit places, and fast loop closing convergence. Moreover, LTA-OM is versatile as it is applicable to both multi-line spinning and solid-state LiDARs, mobile robots and handheld platforms.

This paper presents a precise two-robot collaboration method for 3D self-localization relying on a single rotating camera and onboard accelerometers used to measure the tilt of the robots. This method allows for localization in GPS-denied environments and in the presence of magnetic interference or relatively (or totally) dark and unstructured unmarked locations. One robot moves forward on each step while the other remains stationary. The tilt angles of the robots obtained from the accelerometers and the rotational angle of the turret, associated with the video analysis, make it possible to continuously calculate the location of each robot. We describe a hardware setup used for experiments and provide a detailed description of the algorithm that fuses the data obtained by the accelerometers and cameras and runs in real-time on onboard micro-computers. Finally, we present 2D and 3D experimental results, which show that the system achieves 2% accuracy for the total travelled distance.

Glacial moulins (cylindrical meltwater drainage shafts) provide valuable insights into glacier dynamics, but are inaccessible and hazardous environments for humans to study. Exploring using passive sensor probes has revealed the complex geometry of moulins, however, exploration has been limited. To overcome these challenges, we propose a tethered robot capable of autonomously exploring and capturing data in glacial moulins. Our novel robot is equipped with a tether to support its motion. Combined with novel estimation and control algorithms, the tethered robot is able to safely and efficiently maneuver in confined, chimney-like structures such as moulins. Laboratory and field experiments confirm the feasibility of the proposed design, showing successful localization in environments with no access to positional measurements. Field trials on the Mer de Glace glacier demonstrate the robot’s capabilities, descending into the largest moulin to depths of 25m and using onboard sensors to reconstruct the moulin shape. Two sampling mechanisms are presented and evaluated to extract samples from the icy surface of the moulin. Our results show promising potential for future exploration of moulins, demonstrating the effectiveness of our tethered robot for safely gathering data from these hazardous environments.