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.

翻译：感知与绘制周围环境是实现机器人自主导航的核心能力。同步定位与地图构建（SLAM）算法能在修正多数机器人系统中存在的里程计误差的同时，实现高精度地图构建。目前，全机载地图构建仅能在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。因此，受硬件尺寸限制的微型机器人通常将SLAM任务卸载至外部基础设施。为解决在资源受限处理器上部署SLAM算法的挑战，本文提出NanoSLAM——一种专为厘米级机器人设计的轻量化端到端SLAM方法，其功耗预算仅87.9毫瓦。我们在真实场景中验证了其地图构建能力，并将NanoSLAM部署于自重44克、搭载新型商用RISC-V低功耗并行处理器GAP9的纳米无人机上。该算法通过充分利用RISC-V处理核心的并行计算能力，实现了对通用环境4.5厘米精度的地图构建，端到端执行时间小于250毫秒。