Papers › Interactive Distance Field Mapping and Planning to Enable Human-Robot Collaboration
Interactive Distance Field Mapping and Planning to Enable Human-Robot Collaboration
Usama Ali, Lan Wu, Adrian Mueller, Fouad Sukkar, Tobias Kaupp, Teresa Vidal-Calleja
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
Human-robot collaborative applications require scene representations that are kept up-to-date and facilitate safe motions in dynamic scenes. In this letter, we present an interactive distance field mapping and planning (IDMP) framework that handles dynamic objects and collision avoidance through an efficient representation. We define interactive mapping and planning as the process of creating and updating the representation of the scene online while simultaneously planning and adapting the robot's actions based on that representation. The key aspect of this work is an efficient Gaussian Process field that performs incremental updates and handles dynamic objects reliably by identifying moving points via a simple and elegant formulation based on queries from a temporary latent model. In terms of mapping, IDMP is able to fuse point cloud data from single and multiple sensors, query the free space at any spatial resolution, and deal with moving objects without semantics. In terms of planning, IDMP allows seamless integration with gradient-based reactive planners facilitating dynamic obstacle avoidance for safe human-robot interactions. Our mapping performance is evaluated on both real and synthetic datasets. A comparison with similar state-of-the-art frameworks shows superior performance when handling dynamic objects and comparable or better performance in the accuracy of the computed distance and gradient field. Finally, we show how the framework can be used for fast motion planning in the presence of moving objects both in simulated and real-world scenes. An accompanying video, code, and datasets are made publicly available https://uts-ri.github.io/IDMP.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections