Long‐Range Time‐Correlated Single‐Photon Counting Lidar 3D‐Reconstruction From a Moving Ground Vehicle

Time‐correlated single‐photon counting (TCSPC) lidar enables high‐resolution 3D imaging at kilometer range. While previous works have covered long range TCSPC 3D imaging from either stationary or airborne platforms, this is, to the best of our knowledge, the first attempt that use a moving ground vehicle. Fusing measurements taken at different locations and time imposes very high demands on knowledge of the sensor pose which are hard to achieve on such platforms. In this study we use simultaneous localization and mapping (SLAM) to correct for positioning errors, allowing us to create high fidelity point clouds using long range TCSPC lidar imaging on a moving ground vehicle. Our method uses inertial and GNSS sensors to get an initial estimate of the sensor motion, which is used to reconstruct parts of the scene over short time intervals. The initial motion estimate is then refined by adding constraints from local point cloud matching, and the refined estimate is used to construct the final point cloud of the target area. We describe the sensor system and integration of all sensors, as well as the field trial at which the system was evaluated. The proposed method is able to generate a high fidelity point cloud of a wooded target area from a distance of roughly 800 m while measuring from a moving vehicle. Compared to measurements from a stationary position we obtain better coverage of the target area, and increased ability to penetrate into the forest. However, some precision is lost in the reconstructed point cloud.