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实现自主行动的基本功能。因此,我们提出了一种基于在线建图的新型方法Dynablox,用于在复杂环境中实现鲁棒的移动目标检测。我们方法的核心思想是通过建模和考虑在线机器人操作过程中的感知、状态估计和建图限制来逐步估计高信心的自由空间区域。这种时空保守的自由空间估计使得无需对物体或环境外观做任何假设即可实现鲁棒的移动目标检测。这使得该方法可以部署在复杂场景中,例如多层建筑或楼梯,对于各种移动物体都能够良好地适应,例如人携带各种物品、门扇开关甚至球在周围滚动等等。我们在真实数据集上进行了彻底的评估,在典型的机器人设定下实现了17 FPS的86% IoU。该方法胜过了最近的基于外观的分类器,接近离线方法的性能。我们在一组新的复杂环境中罕见的动态物体数据集上证明了它的通用性。我们提供了我们的高效实现和新数据集的开源代码。