Abstract
SLAM technology has been playing a key role for mobile robots to execute complex tasks such as environment perception, path planning and human-computer interaction. Due to the single sensor has its limitation, the multi-sensor methods that can efficiently improve the performance of the SLAM are becoming the important issues. In this paper, we propose a method based on the fusion of Lidar and monocular vision to improve the positioning accuracy of SLAM. The experimental results in KITTI dataset and Apollo Scape dataset show that the proposed method performs higher positioning accuracy and robustness than existing SLAM methods.
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Li, Y., Zhang, R., Li, Q., Lou, L. (2022). An Improved SLAM Based on the Fusion of Lidar and Monocular Vision. In: Jansen, T., Jensen, R., Mac Parthaláin, N., Lin, CM. (eds) Advances in Computational Intelligence Systems. UKCI 2021. Advances in Intelligent Systems and Computing, vol 1409. Springer, Cham. https://doi.org/10.1007/978-3-030-87094-2_35
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DOI: https://doi.org/10.1007/978-3-030-87094-2_35
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