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：实时检测运动物体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——一种基于在线地图的新型方法，可在复杂环境中实现鲁棒的运动物体检测。该方法的核心思想是通过建模并考虑机器人在线运行过程中的传感、状态估计与建图局限，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无需对物体或环境外观做任何假设，即可实现运动物体的稳健检测。该方法可部署于多层建筑或楼梯等复杂场景，并适用于携带各类物品的人员、摆动门甚至滚动球体等多类型运动物体。我们在真实世界数据集上对该方法进行了全面评估，在典型机器人设置下以17 FPS的运行速度达到86%的IoU。该方法优于近期基于外观的分类器，且性能接近离线方法。我们通过包含复杂环境中稀有运动物体的新数据集验证了其通用性。我们将高效的算法实现及新数据集以开源形式公开。