NanoSLAM：为微型机器人实现完全机载SLAM
NanoSLAM: Enabling Fully Onboard SLAM for Tiny Robots
AI总结：
本文提出NanoSLAM，一种专为资源受限的微型机器人设计的轻量级端到端SLAM方法，在仅87.9 mW功耗下实现4.5 cm建图精度和小于250 ms的执行时间。
摘要：
感知和绘制周围环境对于任何机器人平台实现自主导航都是必不可少的。能够在修正大多数机器人系统中存在的里程计误差的同时实现精确建图的算法类别是同步定位与建图（SLAM）。如今，完全机载建图仅能在可搭载高功耗处理器的机器人平台上实现，这主要是因为执行SLAM算法需要巨大的计算负载和内存需求。出于这个原因，口袋大小的硬件受限机器人将SLAM的执行卸载到外部基础设施。为了应对在资源受限的处理器上启用SLAM算法的挑战，本文提出了NanoSLAM，这是一种轻量级且优化的端到端SLAM方法，专为在厘米级机器人上运行而设计，其功耗预算仅为87.9 mW。我们在真实场景中展示了建图能力，并将NanoSLAM部署在一架重44 g的纳米无人机上，该无人机配备了一款名为GAP9的新型商用RISC-V低功耗并行处理器。该算法旨在利用RISC-V处理核心的并行能力，能够以4.5 cm的精度对一般环境进行建图，且端到端执行时间少于250 ms。
英文摘要：
Perceiving and mapping the surroundings are essential for enabling autonomous navigation in any robotic platform. The algorithm class that enables accurate mapping while correcting the odometry errors present in most robotics systems is Simultaneous Localization and Mapping (SLAM). Today, fully onboard mapping is only achievable on robotic platforms that can host high-wattage processors, mainly due to the significant computational load and memory demands required for executing SLAM algorithms. For this reason, pocket-size hardware-constrained robots offload the execution of SLAM to external infrastructures. To address the challenge of enabling SLAM algorithms on resource-constrained processors, this paper proposes NanoSLAM, a lightweight and optimized end-to-end SLAM approach specifically designed to operate on centimeter-size robots at a power budget of only 87.9 mW. We demonstrate the mapping capabilities in real-world scenarios and deploy NanoSLAM on a nano-drone weighing 44 g and equipped with a novel commercial RISC-V low-power parallel processor called GAP9. The algorithm is designed to leverage the parallel capabilities of the RISC-V processing cores and enables mapping of a general environment with an accuracy of 4.5 cm and an end-to-end execution time of less than 250 ms.
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