Real-time detection of moving objects is an essential capability for robots acting autonomously in dynamic environments. We thus propose Dynablox, a novel online mapping-based approach for robust moving object detection in complex environments. The central idea of our approach is to incrementally estimate high confidence free-space areas by modeling and accounting for sensing, state estimation, and mapping limitations during online robot operation. The spatio-temporally conservative free space estimate enables robust detection of moving objects without making any assumptions on the appearance of objects or environments. This allows deployment in complex scenes such as multi-storied buildings or staircases, and for diverse moving objects such as people carrying various items, doors swinging or even balls rolling around. We thoroughly evaluate our approach on real-world data sets, achieving 86% IoU at 17 FPS in typical robotic settings. The method outperforms a recent appearance-based classifier and approaches the performance of offline methods. We demonstrate its generality on a novel data set with rare moving objects in complex environments. We make our efficient implementation and the novel data set available as open-source.

翻译：实时检测运动目标是机器人在动态环境中自主运行的关键能力。为此，我们提出Dynablox——一种基于在线建图的新型鲁棒运动目标检测方法。该方法的核心思想是通过在机器人在线运行期间对感知、状态估计与建图局限性进行建模与考量，逐步估算高置信度的自由空间区域。这种时空保守的自由空间估计能够在不对目标或环境外观作任何假设的前提下，实现对运动目标的鲁棒检测。因此，该方法可部署于多层建筑、楼梯等复杂场景，并适用于各类运动目标，如携带不同物品的人员、摆动的门甚至滚动的球。我们基于真实数据集对方法进行全面评估，在典型机器人场景中实现了17 FPS下的86% IoU。该方法优于近期基于外观的分类器，性能接近离线方法。我们通过包含复杂环境中罕见运动目标的新数据集验证了其通用性。同时，我们将高效实现代码及该新数据集作为开源资源发布。