This paper proposes a method for CNN-based fault detection of the scan-matching algorithm for accurate SLAM in dynamic environments. When there are dynamic objects in an environment, the environment that is detected by a LiDAR sensor changes. Thus, the scan matching of laser scans is likely to fail. Therefore, a more robust scan-matching algorithm to overcome the faults of scan matching is needed for 2D SLAM. The proposed method first receives raw scan data in an unknown environment and executes ICP (Iterative Closest Points) scan matching of laser scans from a 2D LiDAR. Then, the matched scans are converted into images, which are fed into a CNN model for its training to detect the faults of scan matching. Finally, the trained model detects the faults when new scan data are provided. The training and evaluation are performed in various dynamic environments, taking real-world scenarios into account. Experimental results showed that the proposed method accurately detects the faults of scan matching in every experimental environment.

The present work proposes a method to characterize, calibrate, and compare, any 2D SLAM algorithm, providing strong statistical evidence, based on descriptive and inferential statistics to bring confidence levels about overall behavior of the algorithms and their comparisons. This work focuses on characterize, calibrate, and compare Cartographer, Gmapping, HECTOR-SLAM, KARTO-SLAM, and RTAB-Map SLAM algorithms. There were four metrics in place: pose error, map accuracy, CPU usage, and memory usage; from these four metrics, to characterize them, Plackett-Burman and factorial experiments were performed, and enhancement after characterization and calibration was granted using hypothesis tests, in addition to the central limit theorem.