Title
Dynaslam Ii: Tightly-Coupled Multi-Object Tracking And Slam
Abstract
The assumption of scene rigidity is common in visual SLAM algorithms. However, it limits their applicability in populated real-world environments. Furthermore, most scenarios including autonomous driving, multi-robot collaboration and augmented/virtual reality, require explicit motion information of the surroundings to help with decision making and scene understanding. We present in this paper DynaSLAM II, a visual SLAM system for stereo and RGB-D camera configurations that tightly integrates the multi-object tracking capability. DynaSLAM II makes use of instance semantic segmentation and ORB features to track dynamic objects. The structures of the static scene and the dynamic objects are optimized jointly with the trajectories of both the camera and the moving agents within a novel bundle adjustment proposal. The 3D bounding boxes of the objects are also estimated and loosely optimized within a fixed temporal window. We demonstrate that tracking dynamic objects does not only provide rich clues for scene understanding but can be also beneficial for camera tracking.
Year
DOI
Venue
2021
10.1109/LRA.2021.3068640
IEEE ROBOTICS AND AUTOMATION LETTERS
Keywords
DocType
Volume
Cameras, Simultaneous localization and mapping, Dynamics, Vehicle dynamics, Tracking, Trajectory, Semantics, Dynamic objects, SLAM, semantics, tracking
Journal
6
Issue
ISSN
Citations 
3
2377-3766
1
PageRank 
References 
Authors
0.35
0
4
Name
Order
Citations
PageRank
Berta Bescós1221.32
Carlos Campos2385.14
Juan Domingo33319258.54
José Neira41266136.94