Browse > Article
http://dx.doi.org/10.7746/jkros.2021.16.3.189

Map Error Measuring Mechanism Design and Algorithm Robust to Lidar Sparsity  

Jung, Sangwoo (Dept. of Civil and Environmental Engineering, KAIST)
Jung, Minwoo (Dept. of Civil and Environmental Engineering, KAIST)
Kim, Ayoung (Dept. of Civil and Environmental Engineering, KAIST)
Publication Information
The Journal of Korea Robotics Society / v.16, no.3, 2021 , pp. 189-198 More about this Journal
Abstract
In this paper, we introduce the software/hardware system that can reliably calculate the distance from sensor to the model regardless of point cloud density. As the 3d point cloud map is widely adopted for SLAM and computer vision, the accuracy of point cloud map is of great importance. However, the 3D point cloud map obtained from Lidar may reveal different point cloud density depending on the choice of sensor, measurement distance and the object shape. Currently, when measuring map accuracy, high reflective bands are used to generate specific points in point cloud map where distances are measured manually. This manual process is time and labor consuming being highly affected by Lidar sparsity level. To overcome these problems, this paper presents a hardware design that leverage high intensity point from three planar surface. Furthermore, by calculating distance from sensor to the device, we verified that the automated method is much faster than the manual procedure and robust to sparsity by testing with RGB-D camera and Lidar. As will be shown, the system performance is not limited to indoor environment by progressing the experiment using Lidar sensor at outdoor environment.
Keywords
Point Cloud Map Accuracy Evaluation; Lidar Sparsity; Lidar Intensity; SLAM; Plane Fitting;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, "A density-based algorithm for discovering clusters in large spatial databases with noise," Second International Conference on Knowledge Discovery and Data Mining (KDD'96), pp. 226-231. 1996, [Online], https://dl.acm.org/doi/10.5555/3001460.3001507.
2 S. Huh, S. Cho, and D. H. Shim, "3-D Indoor Navigation and Autonomous Flight of a Micro Aerial Vehicle using a Low-cost LIDAR," The Journal of Korea Robotics Society, vol. 9, no. 3, pp. 154-159, Sept., 2014, DOI: 10.7746/jkros.2014.9.3.154.   DOI
3 M. A. Fischler and R. C. Bolles, "Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography," Communications of the ACM, vol. 24, no. 6, pp. 381-395, Jun., 1981, DOI: 10.1145/358669.358692.   DOI
4 J. Wang and E. Olson, "AprilTag 2: Efficient and robust fiducial detection," 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, South Korea, pp. 4193-4198, 2016, DOI: 10.1109/IROS.2016.7759617.   DOI
5 W. Han, "Research On Analyze Accuracy Of Lidar Data In Surveying Projects," The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Congress, Beijing, China, vol. XXXVII, p. 1253-1256, 2008, [Online], https://www.isprs.org/proceedings/XXXVII/congress/tc4.aspx.
6 T.-B. Kwon and W.-S. Chang, "A New Method for Relative/ Quantitative Comparison of Map Built by SLAM," The Journal of Korea Robotics Society, vol. 9, no. 4, pp. 242-249, Dec., 2014, DOI: 10.7746/jkros.2014.9.4.242.   DOI
7 Y. Cheng, "Mean shift, mode seeking, and clustering," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, no. 8, pp. 790-799, Aug., 1995, DOI: 10.1109/34.400568.   DOI
8 T. Shan, B. Englot, D. Meyers, W. Wang, C. Ratti, and D. Rus, "LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping," 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, USA, pp. 5135-5142, 2020, DOI: 10.1109/IROS45743.2020.9341176.   DOI
9 J. -K. Huang, S. Wang, M. Ghaffari, and J. W. Grizzle, "LiDARTag: A Real-Time Fiducial Tag System for Point Clouds," IEEE Robotics and Automation Letters, vol. 6, no. 3, pp. 4875-4882, Jul., 2021, DOI: 10.1109/LRA.2021.3070302.   DOI