Pedagogy

Mobile robot

Bounding overwatch

RGB color model

Image (mathematics)

Artificial intelligence

Psychology

Bundle adjustment

Tracking (education)

Video tracking

Computer science

Object (grammar)

Simultaneous localization and mapping

Computer vision

Segmentation

Robot

IEEE Robotics and Automation Letters

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.

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