Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/140448
Title: Integrated metric-topological localization by fusing visual odometry, digital map and place recognition
Authors: Yang, Shuai
Jiang, Rui
Wang, Han
Ge, Sam Shuzhi
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2017
Source: Yang, S., Jiang, R., Wang, H., & Ge, S. S. (2018). Integrated metric-topological localization by fusing visual odometry, digital map and place recognition. Proceedings of 5th International Conference on Man-Machine Interactions (ICMMI 2017), 341-352. doi:10.1007/978-3-319-67792-7_34
Abstract: Visual odometry, map-assisted methods and place recognition are all popular approaches to localize a mobile vehicle from three different perspectives. Separate implementation of these methods may cause the localization system vulnerable due to the drift issue and local pose estimation of visual odometry, the on-road assumption and tough initialization of map-assisted methods and the discontinuous output of place recognition. In order to give full play to their advantages, an integrated localization strategy is presented in this paper, where metric data such as visual odometry measurement, a digital map and topological data of place recognition results are incorporated. Place recognition assists initialization process and provides topological place estimation at all times. Gaussian-Gaussian Distribution is used for visual odometry raw measurement representation such that the errors of odometry is appropriately modelled. By comparing similarities between the digital map and odometry trajectories, we then use map-assisted approach to correct odometry estimation. Finally, a mutual check gives a criterion for judging whether metric and topological results are sufficiently consistent. Experiment results show that the integrated system outperforms subsystems with mean localization error at 2.9 m on our self-collected dataset with off-road scenarios.
URI: https://hdl.handle.net/10356/140448
ISBN: 9783319677910
DOI: 10.1007/978-3-319-67792-7_34
Rights: © 2018 Springer International Publishing AG. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Conference Papers

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