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 configurations that tightly integrates the multi-object
tracking capability.
DynaSLAM II makes use of instance semantic segmentation and of ORB features
to track dynamic objects. The structure of the static scene and of the dynamic
objects is 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 is also beneficial for camera tracking.
The project code will be released upon acceptance.

本文介绍了 DynaSLAM II，它是一种用于立体和 RGB-D 配置的视觉 SLAM 系统，紧密集成了多对象跟踪能力，并利用实例语义分割和 ORB 特征来跟踪动态对象，优化了静态场景和动态对象的结构，提供了丰富的场景理解线索，并有益于相机的跟踪。