Real-time manhattan world rotation estimation in 3D
Name
Leonard_Real-time manhattan.pdf
Size
2.99 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Straub, Julian
Bhandari, Nishchal
Leonard, John J
Fisher, John W
Date Issued
January 2016
Journal
Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Straub, Julian et al. “Real-Time Manhattan World Rotation Estimation in 3D.” IEEE, 2015. 1913–1920.
Version
Author's final manuscript
Abstract
Drift of the rotation estimate is a well known problem in visual odometry systems as it is the main source of positioning inaccuracy. We propose three novel algorithms to estimate the full 3D rotation to the surrounding Manhattan World (MW) in as short as 20 ms using surface-normals derived from the depth channel of a RGB-D camera. Importantly, this rotation estimate acts as a structure compass which can be used to estimate the bias of an odometry system, such as an inertial measurement unit (IMU), and thus remove its angular drift. We evaluate the run-time as well as the accuracy of the proposed algorithms on groundtruth data. They achieve zerodrift rotation estimation with RMSEs below 3.4° by themselves and below 2.8° when integrated with an IMU in a standard extended Kalman filter (EKF). Additional qualitative results show the accuracy in a large scale indoor environment as well as the ability to handle fast motion. Selected segmentations of scenes from the NYU depth dataset demonstrate the robustness of the inference algorithms to clutter and hint at the usefulness of the segmentation for further processing.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/IROS.2015.7353628
