室内环境中的偏好多目标搜索：基于语义SLAM
Preferential Multi-Target Search in Indoor Environments using Semantic SLAM
AI总结：
本文提出一种基于语义SLAM的启发式方法，使服务机器人能在室内按用户偏好高效搜索多个目标，并通过真实环境实验验证了其有效性。
摘要：
近年来，对能够执行超越自主导航任务的服务机器人的需求不断增长。未来，服务机器人有望执行诸如“为晚餐摆好餐桌”之类的复杂任务。这类高层任务除了其他能力外，还要求机器人具备检索多个目标的能力。本文深入探讨了在环境中定位多个目标的挑战，即“找到我的物品”任务。我们提出了一种新颖的启发式方法，旨在帮助机器人在室内空间对多个目标进行偏好性搜索。我们的方法涉及一个语义SLAM框架，该框架将语义目标识别与几何数据相结合，以生成多层地图。我们将语义地图与概率先验融合，以实现高效推理。考虑到障碍物可能遮挡导航目标并使标准点对点导航策略可行性降低所带来的挑战，我们的方法对此类因素具有鲁棒性。重要的是，我们的方法适用于各种目标检测器、RGB-D SLAM技术和局部导航规划器。我们在真实室内环境中演示了“找到我的物品”任务，定量结果证明了我们方法的有效性。该策略可应用于服务机器人需要根据用户偏好定位、抓取和搬运物体的场景。简要总结请参阅我们的视频：https://tinyurl.com/PrefTargetSearch
英文摘要：
In recent years, the demand for service robots capable of executing tasks beyond autonomous navigation has grown. In the future, service robots will be expected to perform complex tasks like 'Set table for dinner'. High-level tasks like these, require, among other capabilities, the ability to retrieve multiple targets. This paper delves into the challenge of locating multiple targets in an environment, termed 'Find my Objects.' We present a novel heuristic designed to facilitate robots in conducting a preferential search for multiple targets in indoor spaces. Our approach involves a Semantic SLAM framework that combines semantic object recognition with geometric data to generate a multi-layered map. We fuse the semantic maps with probabilistic priors for efficient inferencing. Recognizing the challenges introduced by obstacles that might obscure a navigation goal and render standard point-to-point navigation strategies less viable, our methodology offers resilience to such factors. Importantly, our method is adaptable to various object detectors, RGB-D SLAM techniques, and local navigation planners. We demonstrate the 'Find my Objects' task in real-world indoor environments, yielding quantitative results that attest to the effectiveness of our methodology. This strategy can be applied in scenarios where service robots need to locate, grasp, and transport objects, taking into account user preferences. For a brief summary, please refer to our video: https://tinyurl.com/PrefTargetSearch
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
