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Improving Visual SLAM in Car-Navigated Urban Environments with Appearance Maps
| Type of publication: | Inproceedings |
| Citation: | jaenal2021improving |
| Booktitle: | 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
| Year: | 2020 |
| Pages: | 4679-4685 |
| DOI: | https://doi.org/10.1109/IROS45743.2020.9341451 |
| Abstract: | This paper describes a method that corrects errors of a VSLAM-estimated trajectory for cars driving in GPS-denied environments, by applying constraints from public databases of geo-tagged images (Google Street View, Mapillary, etc). The method, dubbed Appearance-based Geo-Alignment for Simultaneous Localisation and Mapping (AGA-SLAM), encodes the available image database as an appearance map, which represents the space with a compact holistic descriptor for each image plus its associated geo-tag. The VSLAM trajectory is corrected on-line by incorporating constraints from the recognized places along the trajectory into a position-based optimization framework. The paper presents a seamless formulation to combine local and absolute metric observations with associations from Visual Place Recognition. The robustness of the holistic image descriptor to changes due to weather or illumination variations ensures a long-term consistent method to improve car localization. The proposed method has been extensively evaluated on more than 70 sequences from 4 different datasets, proving out its effectiveness and endurance to appearance challenges. |
| Userfields: | img_url=http%3A%2F%2Fmapir.uma.es%2Fimagesrepo%2Fpapers%2F2021_ajaenal_IROS_improving.png,rank_indexname=,rank_pos_in_category=,rank_num_in_category=,rank_cat_name=,impact_factor= |
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| Authors | |
| Added by: | [] |
| Total mark: | 0 |
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