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LiDAR odometry and mapping (LOAM) has been playing an important role in autonomous vehicles, due to its ability to simultaneously localize the robot's pose and build high-precision, high-resolution maps of the surrounding environment.
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“Ces 2018: Waiting for the $100 lidar.” [Online]. Available: https://spectrum.ieee.org/cars-that-think/transportation/sensors/ces-2018-how-a-new-generation-lidars-is-redefining-the-car
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J. Zhang and S. Singh, “Loam: Lidar odometry and mapping in real-time.” in Robotics: Science and Systems , vol. 2, 2014, p. 9
2014
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E. Mendes, P. Koch, and S. Lacroix, “Icp-based pose-graph slam,” in 2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) . IEEE, 2016, pp. 195–200
2016
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“Point cloud characteristics of livox-lidar.” [Online]. Available: https://www.livoxtech.com/3296f540ecf5458a8829e01cf429798e/downloads/Pointcloudcharacteristics.pdf
Cited in the paper.
F. Gao, W. Wu, W. Gao, and S. Shen, “Flying on point clouds: Online trajectory generation and autonomous navigation for quadrotors in cluttered environments,” Journal of Field Robotics , vol. 36, no. 4, pp. 710–733, 2019
2019
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