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.

翻译：人机协作应用需要能够实时更新并促进动态场景中安全运动的场景表示方法。本文提出一种交互式距离场映射与规划（IDMP）框架，通过高效表征处理动态物体与避碰问题。我们将交互式映射与规划定义为在线创建和更新场景表征，同时基于该表征规划并调整机器人动作的过程。本工作的核心在于构建高效的高斯过程场，该场通过基于临时隐式模型查询的简洁优雅公式识别运动点，实现增量式更新并可靠处理动态物体。在映射方面，IDMP能够融合单/多传感器点云数据，以任意空间分辨率查询自由空间，并处理无语义信息的运动物体。在规划方面，IDMP可与基于梯度的反应式规划器无缝集成，为安全人机交互提供动态避障能力。我们在真实与合成数据集上评估了映射性能，与同类先进框架的对比表明：IDMP在处理动态物体时具有优越性能，在计算距离场与梯度场的精度方面达到相当或更优水平。最后，我们演示了该框架在仿真和真实场景中如何实现存在运动物体时的快速运动规划。相关视频、代码及数据集已公开于 https://uts-ri.github.io/IDMP。