面向稀疏感知的高效二维图SLAM
Efficient 2D Graph SLAM for Sparse Sensing
AI总结：
针对资源受限纳米机器人稀疏含噪测距导致的二维SLAM难题，本文提出含新型前端与后端回环改进的图SLAM方法，可实时运行并生成更高质量地图。
摘要：
同时定位与地图构建（SLAM）在绘制未知空间地图和辅助自主导航方面发挥着至关重要的作用。如今，几乎所有最先进的二维SLAM解决方案都是为激光测距仪（LiDAR）等密集且精确的传感器设计的。然而，这些传感器并不适用于资源受限的纳米机器人；如今纳米机器人的能力日益增强且无处不在，而这类机器人往往搭载经济、低功耗的传感器，只能提供稀疏且含噪的测量。这带来了一个具有挑战性的问题，即稀疏感知SLAM。本工作通过采用最先进的基于图的SLAM流程形式来解决该问题，并提出了一个新颖的前端以及后端回合改进，二者均专为处理稀疏、不确定的距离数据而设计。实验表明，与以往关于稀疏感知的工作相比，我们的算法构建的地图具有更优的质量。此外，我们的方法能够在现代PC上实时运行，平均处理时间仅为输入间隔时间的百分之一。
英文摘要：
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.
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