DynaSLAM II: Tightly-Coupled Multi-Object Tracking and SLAM

By Berta Bescós, Carlos Campos, Juan D. Tardós, José Neira. IEEE Robotics and Automation Letters, 2020

298 citations recorded by the source corpus.

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.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/da306fcecd433588be12b0039682f2dc4ca3432d

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Citations in the corpus

A Comprehensive Survey of Visual SLAM Algorithms (2022, background): “The best SLAM algorithm shall be selected after considering the variety of features and speciﬁcities that this environment and application possess.”
Kimera: From SLAM to spatial perception with 3D dynamic scene graphs (2021, background): “…attempts to gain robustness against dynamic scenes by using an IMU (Hwangbo et al. 2009), masking portions of the scene corresponding to dynamic elements (Cui and Ma 2019; Brasch et al. 2018; Bescos et al. 2018), or jointly tracking camera and dynamic objects (Wang et al. 2007; Bescos et al. 2020).”