即时定位与建图过程中位姿估计算法优化与设 计研究

摘要

障碍物的检测和避免可以被视为设计移动机器人的中心问题，这项技术为机器人提供了可以在不熟悉的环境中穿越而不导致自身损坏。避障机器人可以检测路径上的障碍物并在不发生任何碰撞的情况下进行机动的设计。点集配准算法是移动机器人位姿估计和地图构建中的一个非常重要的算法，广泛应用于模型重建、多视角配准、即时定位与建图(SLAM)等领域，ICP算法已得到国内外研究学者的高度评价。本文比较了迭代最近点(ICP)和基于群集的双向迭代最近点(IDCPBoC)算法。基于以上两种算法，对机器人进行姿态估计，并对移动机器人避障实验过程进行了分析。密集传感器方法(DSM)可构建环境图，IDCPBoC算法可以更好地避免障碍，IDCPBoC算法的准确性和收敛性优于ICP算法，且IDCPBoC算法运行时间较少，因此实时性能也大大提高了。 Obstacle detection and avoidance can be considered as the central issue in designing mobile robots. This technology provides the robots with senses which can use to traverse in unfamiliar environments without damaging itself. In this paper, an Obstacle Avoiding Robot is designed which can detect obstacles in its path and maneuver around them without making any collision. It is a robot vehicle that works on Arduino Microcontroller and employs three ultrasonic distance sensors to detect obstacles. The point set registration algorithm is a very important algorithm in mobile robot pose estimation and map construction. It is widely used in the fields of model reconstruction, multi-view registration, simultaneous localization and mapping (SLAM), etc. The ICP algorithm has been studied at home and abroad. Scholars attach great importance to it. This paper compares the iterative closest point (ICP) and cluster-based iterative bidirectional closest point (IDCPBoC) algorithms. The two algorithms are used to estimate the pose of the robot; the obstacle avoidance experiment process of the mobile robot is analyzed; the environment map is constructed using the DSM (dense sensor method) method; the IDCPBoC algorithm can better avoid obstacles. The accuracy and convergence of the IDCPBoC algorithm are better than the ICP algorithm. Because the IDCPBoC algorithm runs less time, the real-time performance is also greatly improved.