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Loose Coupling Visual-lidar odometry by combining VISO2 and LOAM

Loose Coupling Visual-lidar Odometry by combining VISO2 and ...

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第36届中国控制会议

作者： Min Yan Junzheng Wang Jing Li Chi Zhang Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology

ISBN: (纸本)9781538629185

odometry is a very popular research direction in recent decades and plays a very critical role in the autonomous navigation ***02,as a relatively robust and mature visual odometry algorithm,becomes unreliable when distinct visual features are insufficiently or when the texture of the environment is *** if not in such a challenging environment,the drift is still a big problem for the practical ***,a lidar odometry algorithm,has been leading the way for the performance on KITTI data *** fact,it also has limitations in shape features not prominent environment,like a highway with few buildings *** that the current mobile unmanned platforms（e.g.,unmanned vehicles） are almost equipped with both lidar and cameras,this paper proposes a loose coupling visual-lidar odometry by combining VIS02 and LOAM to achieve the advantages of both camera and lidar and reduce the number of restricted *** algorithm proposed in this paper is tested on the raw data set of *** average translation error is about 1.37% on 10 sequences,and the accuracy is improved with respect to LOAM with the same *** the help of VIS02,the algorithm also performs well in shape features not prominent environment（e.g.,highway）,and from the experiment results,we confirmed the efficient strategy to combine VIS02 and LOAM to adapt to challenging environment and to keep the accuracy of the algorithm.

关键词： Ego-motion Estimation Visual odometry lidar odometry SLAM

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lidar odometry Key Frame Selection Based on Displacement Vector Similarity

Lidar Odometry Key Frame Selection Based on Displacement Vec...

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第40届中国控制会议

作者： Fang Ou Yunhui Li Zhonghua Miao Jin Zhou School of Mechatronic Engineering and Automation Shanghai University

In order to improve the efficiency and reduce the cumulative error of lidar odometry caused by key frame update. This paper proposes a key frame selection method based on displacement vector similarity. First, calculate the pose between the current frame and the reference key frame according to the Normal Distribution Transform（NDT） algorithm;then, set the Manhattan distance between the two frames above, and calculate the cosine similarity of the displacement vector;finally, update the key frame according to the similarity threshold. The experimental results show that the key frame selection method proposed in this paper can effectively improve the key frame update efficiency and reduce the cumulative error of the odometry: compared with the NDT method updating key frame based on the distance, the maximum absolute error is reduced by 11.88 meters, and the relative error is reduced by 8%,time consumption has been reduced by 55 seconds.

关键词： lidar odometry Cosine Similarity Normal Distributions Transform(NDT)

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