3D Surfel Map-Aided Visual Relocalization with Learned Descriptors
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Abstract (arXiv)
In this paper, we introduce a method for visual relocalization using the geometric information from a 3D surfel map. A visual database is first built by global indices from the 3D surfel map rendering, which provides associations between image points and 3D surfels. Surfel reprojection constraints are utilized to optimize the keyframe poses and map points in the visual database. A hierarchical camera relocalization algorithm then utilizes the visual database to estimate 6-DoF camera poses. Learned descriptors are further used to improve the performance in challenging cases. We present evaluation under real-world conditions and simulation to show the effectiveness and efficiency of our method, and make the final camera poses consistently well aligned with the 3D environment.
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References · 14
- 2017CARLA: An Open Urban Driving Simulatorcited by 2,449
- 2017SuperPoint: Self-Supervised Interest Point Detection and Descriptioncited by 1,320
- 2015ORB-SLAM: a Versatile and Accurate Monocular SLAM Systemcited by 988
- 2015NetVLAD: CNN architecture for weakly supervised place recognitioncited by 941
- 2020ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAMcited by 878
- 2016ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Camerascited by 806
- 2018From Coarse to Fine: Robust Hierarchical Localization at Large Scalecited by 559
- 2016DSAC - Differentiable RANSAC for Camera Localizationcited by 266
- 2019Understanding the Limitations of CNN-based Absolute Camera Pose Regressioncited by 177
- 2020KFNet: Learning Temporal Camera Relocalization using Kalman Filteringcited by 43
- 2019Tightly Coupled 3D Lidar Inertial Odometry and Mappingcited by 34
- 2018Leveraging Deep Visual Descriptors for Hierarchical Efficient Localizationcited by 29
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