  LiDAR odometry can achieve accurate vehicle pose estimation for short driving
range or in small-scale environments, but for long driving range or in
large-scale environments, the accuracy deteriorates as a result of cumulative
estimation errors. This drawback necessitates the inclusion of loop closure
detection in a SLAM framework to suppress the adverse effects of cumulative
errors. To improve the accuracy of pose estimation, we propose a new
LiDAR-based SLAM method which uses F-LOAM as LiDAR odometry, Scan Context for
loop closure detection, and GTSAM for global optimization. In our approach, an
adaptive distance threshold (instead of a fixed threshold) is employed for loop
closure detection, which achieves more accurate loop closure detection results.
Besides, a feature-based matching method is used in our approach to compute
vehicle pose transformations between loop closure point cloud pairs, instead of
using the raw point cloud obtained by the LiDAR sensor, which significantly
reduces the computation time. The KITTI dataset is used for verifications of
our method, and the experimental results demonstrate that the proposed method
outperforms typical LiDAR odometry/SLAM methods in the literature. Our code is
made publicly available for the benefit of the community.
