Simultaneous Localization and Mapping (SLAM) estimates agents' trajectories and constructs maps, and localization is a fundamental kernel in autonomous machines at all computing scales, from drones, AR, VR to self-driving cars. In this work, we present an energy-efficient and runtime-reconfigurable FPGA-based accelerator for robotic localization. We exploit SLAM-specific data locality, sparsity, reuse, and parallelism, and achieve >5x performance improvement over the state-of-the-art. Especially, our design is reconfigurable at runtime according to the environment to save power while sustaining accuracy and performance.

翻译：同步本地化和绘图(SLAM)估算代理器的轨迹和构造地图,本地化是所有计算尺度,从无人机、AR、VR到自驾驶车等自主机器的基本内核。在这项工作中,我们为机器人本地化提供了一个节能和可运行时间可配置的基于FPGA的机器人本地化加速器。我们利用SLAM特定数据地点、空间、再利用和平行,并实现了超过5x的性能改进。特别是,我们的设计可以在运行时根据环境进行重组,以便在保持准确性和性能的同时节能。