Gao Ruilan, Wan Zeyu, Guo Sitong, Jiang Changjian, Zhang Yu
State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China.
Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province, Hangzhou 310027, China.
Biomimetics (Basel). 2023 Sep 5;8(5):410. doi: 10.3390/biomimetics8050410.
Simultaneous localization and mapping (SLAM) is one of the crucial techniques applied in autonomous robot navigation. The majority of present popular SLAM algorithms are built within probabilistic optimization frameworks, achieving high accuracy performance at the expense of high power consumption and latency. In contrast to robots, animals are born with the capability to efficiently and robustly navigate in nature, and bionic SLAM algorithms have received increasing attention recently. Current bionic SLAM algorithms, including RatSLAM, with relatively low accuracy and robustness, tend to fail in certain challenging environments. In order to design a bionic SLAM system with a novel framework and relatively high practicality, and to facilitate the development of bionic SLAM research, in this paper we present LFVB-BioSLAM, a bionic SLAM system with a light-weight LiDAR-based front end and a bio-inspired vision-based back end. We adopt a range flow-based LiDAR odometry as the front end of the SLAM system, providing the odometry estimation for the back end, and we propose a biologically-inspired back end processing algorithm based on the monocular RGB camera, performing loop closure detection and path integration. Our method is verified through real-world experiments, and the results show that LFVB-BioSLAM outperforms RatSLAM, a vision-based bionic SLAM algorithm, and RF2O, a laser-based horizontal planar odometry algorithm, in terms of accuracy and robustness.
同时定位与地图构建（SLAM）是自主机器人导航中应用的关键技术之一。当前大多数流行的SLAM算法是在概率优化框架内构建的，以高功耗和高延迟为代价实现了高精度性能。与机器人不同，动物天生具有在自然环境中高效且稳健地导航的能力，仿生SLAM算法最近受到了越来越多的关注。当前的仿生SLAM算法，包括RatSLAM，精度和稳健性相对较低，在某些具有挑战性的环境中容易失败。为了设计一个具有新颖框架和较高实用性的仿生SLAM系统，并促进仿生SLAM研究的发展，在本文中我们提出了LFVB-BioSLAM，这是一个具有基于轻量级激光雷达的前端和受生物启发的基于视觉的后端的仿生SLAM系统。我们采用基于距离流的激光雷达里程计作为SLAM系统的前端，为后端提供里程计估计，并且我们提出了一种基于单目RGB相机的受生物启发的后端处理算法，用于执行回环检测和路径积分。我们的方法通过实际实验得到了验证，结果表明LFVB-BioSLAM在精度和稳健性方面优于基于视觉的仿生SLAM算法RatSLAM和基于激光的水平平面里程计算法RF2O。
