光達與慣性感測器融合之定位與建圖於無人機自主導航之應用

Tightly-Coupled Lidar-Inertial SLAM with Autonomous Navigation for Quadrotors

楊榮程(Jung-Cheng Yang)

指導教授 ： 程登湖

國立交通大學/工學院/工學院機器人碩士學位學程/碩士(2020年)

摘要

近幾年，機器人相關領域漸漸受到矚目。SLAM這個領域已經被研究一段時間，是一項機器人完成高難度任務所需的基礎。然而，感測器融合的重要性在近幾年才開始被研究。
光達與慣性感測器融合之定位與建圖系統在這篇論文中被提出，其中也包含回環偵測及全局位姿優化。光達與慣性感測器被使用來互相補足對方的缺點，達到更好的位置與姿態估測。在SLAM系統中，估測出的位姿及地圖提供機器人環境資訊，使其實現搜索式路徑規劃。在同時考慮環境中障礙物與自身的運動限制下，搜索式路徑規劃可以產生出一條平滑且最短時間的軌跡。
最後，這個光達與慣性感測器系統在KITTI資料集中測是，以評估其方法之精確度。同時也在室內環境中，在無人機上實現搜索式路徑規劃，達到實時地產生一條局部最佳的路徑。

關鍵字

同步定位與建圖 ； 自動駕駛 ； 四軸無人機

並列摘要

In recent years, the field of robotics has attracted lots of attention. For the robot to perform high level tasks, SLAM becomes a fundamental technique to build on. The SLAM problem has been well studied for a period of time, but the importance of sensor fusion to complement each other was just investigated in the past ten years.
In this work, a tightly-coupled lidar-inertial SLAM system is developed with loop-closure and pose graph optimization. The estimate pose and map from the SLAM system provide the knowledge of the environment for the search-based motion planning method. By taking the obstacles in the surrounding and the motion constraints into consideration, a smooth and minimum-time trajectory is generated.
Finally, the lidar-inertial SLAM system is evaluated in KITTI dataset, and an indoor flight experiment has proven the capability of generating a locally-optimal trajectory in real-time.

並列關鍵字

SLAM ； Autonomous Navigation ； Quadrotors

參考文獻

[1] J. Zhang and S. Singh, “Loam: Lidar odometry and mapping in real-time,” in Proceedings of Robotics: Science and Systems Conference, July 2014.

[2] H. Ye, Y. Chen, and M. Liu, “Tightly coupled 3d lidar inertial odometry and mapping,” in 2019 International Conference on Robotics and Automation (ICRA), May 2019, pp. 3144–3150.

[3] Cedric Le Gentil, Teresa A. Vidal-Calleja, and Shoudong Huang, "IN2LAAMA: INertial Lidar Localisation Autocalibration And MApping", ArXiv abs/1905.09517 (2019).

[4] S. Zhao, Z. Fang, H. Li and S. Scherer, "A Robust Laser-Inertial Odometry and Mapping Method for Large-Scale Highway Environments," 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China, 2019, pp. 1285-1292, doi: 10.1109/IROS40897.2019.8967880.

[5] C. Qin, H. Ye, C. E. Pranata, J. Han, and M. Liu, “LINS: A lidar-inerital state estimator for robust and fast navigation,” CoRR, vol. abs/1907.02233, 2019.