Dual-SLAM: A framework for robust single camera navigation


Huajian Huang1 Wen-Yan Lin2 Siying Liu3 Dong Zhang4 Sai-Kit Yeung1

1Hong Kong University of Science and Technology 2Singapore Management University
3Institute for Infocomm Research, Singapore 4Sun Yat-San University, China

IEEE International Conference on Intelligent Robots and Systems (IROS), 2020.

Abstract

SLAM (Simultaneous Localization And Mapping) seeks to provide a moving agent with real-time self-localization. To achieve real-time speed, SLAM incrementally propagates position estimates. This makes SLAM fast but also makes it vulnerable to local pose estimation failures. As local pose estimation is ill-conditioned, local pose estimation failures happen regularly, making the overall SLAM system brittle. This paper attempts to correct this problem. We note that while local pose estimation is ill-conditioned, pose estimation over longer sequences is well-conditioned. Thus, local pose estimation errors eventually manifest themselves as mapping inconsistencies. When this occurs, we save the current map and activate two new SLAM threads. One processes incoming frames to create a new map and the other, recovery thread, backtracks to link new and old maps together. This creates a Dual-SLAM framework that maintains real-time performance while being robust to local pose estimation failures. Evaluation on benchmark datasets shows Dual-SLAM can reduce failures by a dramatic 88%.

Video

Results

Figure10: Sequence 41 of TUM-Mono dataset. Reconstructed 3D points are shown in black, key-frames in blue, linkages between adjacent frames in green and breakage locations in red. Dual-SLAM is stable on this very difficult sequence, allowing it to recover much more of the map than the original ORB-SLAM.

Figure 8: Overlay of SLAM and ground-truth trajectories on KITTI sequences. Ground-truth is in blue. Ideally, the SLAM trajectory would completely cover the ground-truth, making it no longer visible. In cases where the SLAM breaks, large sections of ground-truth become visible. Observe that Dual-SLAM greatly improves ORB-SLAM’s results, making it competitive with LDSO.

Citation

@inproceedings{hhuang2020dualslam,
	title = {{Dual-SLAM}: A framework for robust single camera navigation},
	author = {Huajian Huang, Wen-Yan Lin, Siying Liu, Dong Zhang, Sai-Kit Yeung},
    	booktitle = {Intelligent Robots and Systems (IROS), 2020 IEEE international conference on},
    	year = {2020}
	}

Acknowledgements
This research is supported by the Singapore Ministry of Education (MOE) Academic Research Fund (AcRF) Tier 1 grant and internal grant from HKUST(R9429). We also thank Weibin Li and Miaoxin Huang for their generous help.