Monocular Direct Sparse Localization in a Prior 3D Surfel Map
Authors: Haoyang Ye, Huaiyang Huang, and Ming Liu from RAM-LAB.
Paper and Video
Related publications:
@inproceedings{ye2020monocular,
title={Monocular direct sparse localization in a prior 3d surfel map},
author={Ye, Haoyang and Huang, Huaiyang and Liu, Ming},
booktitle={2020 IEEE International Conference on Robotics and Automation (ICRA)},
pages={8892--8898},
year={2020},
organization={IEEE}
}
@inproceedings{ye20213d,
title={3D Surfel Map-Aided Visual Relocalization with Learned Descriptors},
author={Ye, Haoyang and Huang, Huaiyang and Hutter, Marco and Sandy, Timothy and Liu, Ming},
booktitle={2021 International Conference on Robotics and Automation (ICRA)},
pages={5574-5581},
year={2021},
organization={IEEE}
}
Build LiDAR map and obtain LiDAR poses (the poses are not necessary).
Pre-process LiDAR map to make the [path_to_dataset]/*.pcd map file contains normal_x, normal_y, normal_z fields (downsample & normal estimation).
Extract and undistort images into [path_to_dataset]/images.
Set the first camera pose to initial_pose and other camera parameters in [path_to_dataset]/config.yaml.
Note
This implementation of DSL takes Ceres Solver as backend, which is different from the the implementation of the original paper with DSO-backend. This leads to different performance, i.e., speed and accuracy, compared to the reported results.
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