Simultaneous localization and mapping (SLAM) plays a vital role in mapping unknown spaces and aiding autonomous navigation. Virtually all state-of-the-art solutions today for 2D SLAM are designed for dense and accurate sensors such as laser range-finders (LiDARs). However, these sensors are not suitable for resource-limited nano robots, which become increasingly capable and ubiquitous nowadays, and these robots tend to mount economical and low-power sensors that can only provide sparse and noisy measurements. This introduces a challenging problem called SLAM with sparse sensing. This work addresses the problem by adopting the form of the state-of-the-art graph-based SLAM pipeline with a novel frontend and an improvement for loop closing in the backend, both of which are designed to work with sparse and uncertain range data. Experiments show that the maps constructed by our algorithm have superior quality compared to prior works on sparse sensing. Furthermore, our method is capable of running in real-time on a modern PC with an average processing time of 1/100th the input interval time.

翻译：同步定位与地图构建（SLAM）在未知空间映射和自主导航辅助中发挥着关键作用。当前几乎所有最先进的二维SLAM解决方案都设计用于激光测距仪（LiDAR）等高密度高精度传感器。然而，这类传感器并不适用于资源受限的纳米机器人——这些日益强大且广泛应用的设备通常搭载低成本、低功耗的传感器，仅能提供稀疏且带有噪声的测量数据。这引入了一个被称为稀疏感知SLAM的挑战性问题。本研究通过采用最先进的基于图的SLAM流程架构，结合新型前端模块和后端闭环检测改进技术来解决该问题，两者均针对稀疏且不确定的测距数据特性进行设计。实验表明，与先前的稀疏感知研究相比，本算法构建的地图具有更优质量。此外，该方法能在现代PC上实现实时运行，其平均处理时间仅为输入间隔时间的1/100。