# DynaSLAM II: Tightly-Coupled Multi-Object Tracking and SLAM Source: [DynaSLAM II: Tightly-Coupled Multi-Object Tracking and SLAM]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:56:44.820Z Authors: Berta Bescos, Carlos Campos, Juan D. Tard\\'os, Jos\\'e Neira Published: 2020-10-15T15:25:30.000Z ## 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 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [SLAM Trajectory Accuracy on TUM-RGBD (fr3 sequences)](): SLAM; TUM-RGBD; XYZ Error (fr3, w): 1.64. ## Original sources and implementation [Original paper]() [PDF]()