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 \textit{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. Given depth sensor data, our framework builds a continuous field that allows to query the distance and gradient to the closest obstacle at any required position in 3D space. The key aspect of this work is an efficient Gaussian Process field that performs incremental updates and implicitly handles dynamic objects with a simple and elegant formulation based on 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 motion planners facilitating fast re-planning for collision-free navigation. The framework 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. An accompanying video, code, and datasets are made publicly available https://uts-ri.github.io/IDMP.

翻译：人机协作应用需要保持场景表示实时更新，并支持动态场景中的安全运动。本文提出交互式距离场建图与规划（IDMP）框架，通过高效表示处理动态物体和碰撞规避。我们将"交互式"建图与规划定义为：在线创建和更新场景表示的同时，基于该表示规划并自适应调整机器人动作的过程。基于深度传感器数据，该框架构建连续场，可在三维空间任意位置查询最近障碍物的距离与梯度。本研究的关键在于采用高效高斯过程场，通过基于临时潜模型的简洁优雅公式实现增量更新并隐式处理动态物体。在建图方面，IDMP能够融合单传感器与多传感器点云数据，以任意空间分辨率查询自由空间，并在无语义条件下处理移动物体。在规划方面，IDMP支持与基于梯度的运动规划器无缝集成，实现快速重规划以实现无碰撞导航。该框架已在真实数据集与合成数据集上完成评估。与同类先进框架的比较表明，本框架在动态物体处理方面表现更优，在距离与梯度场计算精度方面具有可比或更优性能。最后，我们展示了该框架在存在移动物体场景下的快速运动规划能力。配套视频、代码及数据集已公开于 https://uts-ri.github.io/IDMP。