Self-optimizing loop sifting and majorization for 3D reconstruction
This addresses a bottleneck in vSLAM for autonomous vehicles and robots by improving loop optimization, though it appears incremental as it builds on existing methods.
The paper tackles the problem of conservative loop detection in vSLAM and 3D reconstruction, which often rejects correct loops and reduces performance, by proposing an algorithm that sifts and majorizes loops using a dense map posterior metric without user-defined thresholds, resulting in outperforming state-of-the-art methods on public datasets.
Visual simultaneous localization and mapping (vSLAM) and 3D reconstruction methods have gone through impressive progress. These methods are very promising for autonomous vehicle and consumer robot applications because they can map large-scale environments such as cities and indoor environments without the need for much human effort. However, when it comes to loop detection and optimization, there is still room for improvement. vSLAM systems tend to add the loops very conservatively to reduce the severe influence of the false loops. These conservative checks usually lead to correct loops rejected, thus decrease performance. In this paper, an algorithm that can sift and majorize loop detections is proposed. Our proposed algorithm can compare the usefulness and effectiveness of different loops with the dense map posterior (DMP) metric. The algorithm tests and decides the acceptance of each loop without a single user-defined threshold. Thus it is adaptive to different data conditions. The proposed method is general and agnostic to sensor type (as long as depth or LiDAR reading presents), loop detection, and optimization methods. Neither does it require a specific type of SLAM system. Thus it has great potential to be applied to various application scenarios. Experiments are conducted on public datasets. Results show that the proposed method outperforms state-of-the-art methods.