Mixed 2D/3D Perception for Autonomous Robots in Unstructured Environments
Autonomous robots in real world applications have to deal with a complex 3D environment, but are often equipped with standard 2D laser range finders (LRF) only. By using the 2D LRF for both, the 2D localization and mapping (which can be done efficiently and precisely) and for the 3D obstacle detection (which makes the robot move safely), a completely autonomous robot can be built with affordable 2D LRFs. We use the 2D LRF to perform particle filter based SLAM to generate a 2D occupancy grid, and the same LRF (moved by two servo motors) to acquire 3D scans to detect obstacles not visible in the 2D scans. The 3D data is analyzed with a recursive principal component analysis (PCA) based method, and the detected obstacles are recorded in a separate obstacle map. This obstacle map and the occupancy map are merged for the path planning. Our solution was tested on our mobile system Robbie during the RoboCup Rescue competitions in 2008 and 2009, winning the mapping challenge at the world championship 2008 and the German Open in 2009.
This shows that the benefit of a sensor can dramatically be increased by actively controlling it, and that mixed 2D/3D perception can efficiently be achieved with a standard 2D sensor by controlling it actively.
KeywordsPoint Cloud Autonomous Robot Servo Motor Laser Range Finder Occupancy Grid
Unable to display preview. Download preview PDF.
- [Elfes, 1989]Elfes, A.: Occupancy grids: A probabilistic framework for robot perception and navigation. PhD thesis, Carnegie Mellon University (1989)Google Scholar
- [Kadous et al., 2006]Kadous, M.W., Sammut, C., Sheh, R.: Autonomous traversal of rough terrain using behavioural cloning. In: The 3rd International Conference on Autonomous Robots and Agents (2006)Google Scholar
- [Lalonde et al., 2006]Lalonde, J.-F., Vandapel, N., Huber, D., Hebert, M.: Natural terrain classification using three-dimensional ladar data for ground robot mobility 23(1), 839–861 (2006)Google Scholar
- [Montemerlo et al., 2008]Montemerlo, M., Becker, J., Bhat, S., Dahlkamp, H., Dolgov, D., Ettinger, S., Hähnel, D., Hilden, T., Hoffmann, G., Huhnke, B., Johnston, D., Klumpp, S., Langer, D., Levandowski, A., Levinson, J., Marcil, J., Orenstein, D., Paefgen, J., Penny, I., Petrovskaya, A., Pflueger, M., Stanek, G., Stavens, D., Vogt, A., Thrun, S.: Junior: The stanford entry in the urban challenge. J. Field Robotics 25(9), 569–597 (2008)CrossRefGoogle Scholar
- [Nüchter, 2006]Nüchter, A.: Semantische dreidimensionale Karten für autonome mobile Roboter. PhD thesis, Rheinische Friedrich-Wilhelms-Universität Bonn (2006)Google Scholar
- [Ohno et al., 2008]Ohno, K., Kawahara, T., Tadokoro, S.: Development of 3d laser scanner for measuring uniform and dense 3d shapes of static objects in dynamic environment. In: ROBIO 2008: IEEE International Conference on Robotics and Biomimetics, February 22-25, pp. 2161–2167 (2009)Google Scholar
- [Pellenz, 2007]Pellenz, J.: Rescue robot sensor design: An active sensing approach. In: SRMED 2007: Fourth International Workshop on Synthetic Simulation and Robotics to Mitigate Earthquake Disaster, Atlanta, USA, pp. 33–37 (2007)Google Scholar
- [Sheh et al., 2007]Sheh, R., Kadous, M., Sammut, C., Hengst, B.: Extracting terrain features from range images for autonomous random stepfield traversal. In: IEEE International Workshop on Safety, Security and Rescue Robotics, SSRR 2007, pp. 1–6 (2007)Google Scholar
- [Thrun, 2006]Thrun, S.: Winning the darpa grand challenge: A robot race through the mojave desert. In: ASE 2006: Proceedings of the 21st IEEE/ACM International Conference on Automated Software Engineering, p. 11. IEEE Computer Society, Washington (2006)Google Scholar
- [Wirth and Pellenz, 2007]Wirth, S., Pellenz, J.: Exploration transform: A stable exploring algorithm for robots in rescue environments. In: Workshop on Safety, Security, and Rescue Robotics, pp. 1–5 (2007), http://sied.dis.uniroma1.it/ssrr07/