Torres-González Arturo, Martínez-de Dios Jose Ramiro, Ollero Anibal
Robotics Vision and Control Group, University of Sevilla, Escuela Superior de Ingenieros, c/Camino de los Descubrimientos s/n, 41092 Seville, Spain.
Sensors (Basel). 2017 Apr 20;17(4):903. doi: 10.3390/s17040903.
This work deals with robot-sensor network cooperation where sensor nodes (beacons) are used as landmarks for Range-Only (RO) Simultaneous Localization and Mapping (SLAM). Most existing RO-SLAM techniques consider beacons as passive devices disregarding the sensing, computational and communication capabilities with which they are actually endowed. SLAM is a resource-demanding task. Besides the technological constraints of the robot and beacons, many applications impose further resource consumption limitations. This paper presents a scalable distributed RO-SLAM scheme for resource-constrained operation. It is capable of exploiting robot-beacon cooperation in order to improve SLAM accuracy while meeting a given resource consumption bound expressed as the maximum number of measurements that are integrated in SLAM per iteration. The proposed scheme combines a Sparse Extended Information Filter (SEIF) SLAM method, in which each beacon gathers and integrates robot-beacon and inter-beacon measurements, and a distributed information-driven measurement allocation tool that dynamically selects the measurements that are integrated in SLAM, balancing uncertainty improvement and resource consumption. The scheme adopts a robot-beacon distributed approach in which each beacon participates in the selection, gathering and integration in SLAM of robot-beacon and inter-beacon measurements, resulting in significant estimation accuracies, resource-consumption efficiency and scalability. It has been integrated in an octorotor Unmanned Aerial System (UAS) and evaluated in 3D SLAM outdoor experiments. The experimental results obtained show its performance and robustness and evidence its advantages over existing methods.
这项工作涉及机器人 - 传感器网络协作，其中传感器节点（信标）被用作仅测距（RO）同步定位与地图构建（SLAM）的地标。大多数现有的RO - SLAM技术将信标视为被动设备，而忽略了它们实际具备的传感、计算和通信能力。SLAM是一项资源需求大的任务。除了机器人和信标的技术限制外，许多应用还对资源消耗施加了进一步的限制。本文提出了一种用于资源受限操作的可扩展分布式RO - SLAM方案。它能够利用机器人 - 信标协作来提高SLAM精度，同时满足以每次迭代中集成到SLAM中的最大测量次数表示的给定资源消耗界限。所提出的方案结合了一种稀疏扩展信息滤波器（SEIF）SLAM方法，其中每个信标收集并整合机器人 - 信标和信标间的测量，以及一种分布式信息驱动的测量分配工具，该工具动态选择集成到SLAM中的测量，平衡不确定性改善和资源消耗。该方案采用机器人 - 信标分布式方法，其中每个信标参与机器人 - 信标和信标间测量在SLAM中的选择、收集和整合，从而实现显著的估计精度、资源消耗效率和可扩展性。它已集成到一个八旋翼无人机系统（UAS）中，并在3D SLAM户外实验中进行了评估。获得的实验结果展示了其性能和鲁棒性，并证明了它相对于现有方法的优势。
