LiDAR Based SLAM: A Comparative Evaluation with LeGO-LOAM and HDL-Graph SLAM

Simultaneous localization and mapping (SLAM) is an area of research that is experiencing rapid advancements, with a significant impact on improving the navigation and perception capabilities of vehicles, thereby enabling their safe operation in complex environments. This study presents a comprehensive overview of the recent developments in SLAM and conducts a comparative evaluation of two widely employed SLAM methods. The evaluation is based on rigorous performance analysis using the KITTI dataset, which is one of the most popular benchmark datasets for evaluating SLAM algorithms. The evaluation focuses on two essential metrics: absolute trajectory error (ATE) and relative pose error (RPE), which provides valuable insights into the accuracy and consistency of pose estimation over time. By quantifying the deviation between estimated trajectories and ground truth data, ATE sheds light on the global accuracy of the SLAM system, while RPE examines the local error and the system's ability to maintain reliable pose estimates within sequential frames. A thorough discussion is provided on the advantages, distinctive features, and performance characteristics of each technique, thereby offering valuable insights to propel future research in this area.