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A flexible and scalable SLAM system with full 3D motion estimation
Abstract
For many applications in Urban Search and Rescue (USAR) scenarios robots need to learn a map of unknown environments. We present a system for fast online learning of occupancy grid maps requiring low computational resources. It combines a robust scan matching approach using a LIDAR system with a 3D attitude estimation system based on inertial sensing. By using a fast approximation of map gradients and a multi-resolution grid, reliable localization and mapping capabilities in a variety of challenging environments are realized. Multiple datasets showing the applicability in an embedded hand-held mapping system are provided. We show that the system is sufficiently accurate as to not require explicit loop closing techniques in the considered scenarios. The software is available as an open source package for ROS.
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Citations
Cited by
LOAM: Lidar Odometry and Mapping in Real-time
Zhang, J., & Singh, S. (2014) · Robotics: Science and Systems X · doi:10.15607/rss.2014.x.007 · Cited by 3,303
Analysis and Observations From the First Amazon Picking Challenge
Correll, N., Bekris, K. E., Berenson, D., Brock, O., Causo, A., Hauser, K., Okada, K., Rodriguez, A., Romano, J. M., & Wurman, P. R. (2018) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2016.2600527 · Cited by 425
Autonomous driving system: A comprehensive survey
Zhao, J., Zhao, W., Deng, B., Wang, Z., Zhang, F., Zheng, W., Cao, W., Nan, J., Lian, Y., & Burke, A. F. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122836 · Cited by 361
Multiple-Robot Simultaneous Localization and Mapping: A Review
Saeedi, S., Trentini, M., Seto, M., & Li, H. (2016) · Journal of Field Robotics · doi:10.1002/rob.21620 · Cited by 324
A Comparative Analysis of LiDAR SLAM-Based Indoor Navigation for Autonomous Vehicles
Zou, Q., Sun, Q., Chen, L., Nie, B., & Li, Q. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3063477 · Cited by 321
Metadata
- Authors
- Kohlbrecher, S., von Stryk, O., Meyer, J., & Klingauf, U.
- Venue
- 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics
- Publisher
- IEEE
- Published
- 2011-11
- Pages
- 155-160
- OpenAlex
- W1967740178
- Cited by
- 1,086
- References
- 0
- FWCI
- 41.53
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Kohlbrecher, S., von Stryk, O., Meyer, J., & Klingauf, U. (2011). A flexible and scalable SLAM system with full 3D motion estimation. 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics. https://doi.org/10.1109/ssrr.2011.6106777
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