一种优化的移动机器人ALV视觉归航算法

An Optimized Visual Homing Algorithm Based on Average Landmark Vector for Mobile Robot

针对移动机器人平均路标向量(ALV)的算法性能受自然路标影响较大的问题,提出了一种优化算法.在利用图像特征检测与匹配手段,如SIFT(尺度不变特征变换)、SURF(加速鲁棒特征)等,来获得自然路标的前提下,优化算法首先对原始的ALV算法进行了过程拆解,获得归航子向量;然后利用统计学理论对归航子向量的贡献度进行调整,并剔除误匹配路标;最后将带有权值信息的归航子向量重新整合,获得指向目标位置的归航向量.实验表明,优化的ALV算法有效地提高了自然路标的整体精度,保证了路标的对应性,从而提高了ALV算法的准确性,使机器人可以以更理想的轨迹自主地到达目标位置.

Aiming at the problem that the performance of ALV(average landmark vector) algorithm for mobile robots is greatly affected by natural landmarks, an optimized algorithm is proposed. By utilizing the image feature detection and matching algorithms(such as scale-invariant feature transform and speeded-up robust feature) to obtain natural landmarks,the optimized algorithm firstly disassembles the original ALV algorithm and obtains the home sub-vectors. Then, the contributions of the home sub-vectors are adjusted and the mismatching landmarks are eliminated by using the statistical theory.Finally, the home sub-vectors that contain weight information are integrated into the home vector pointing to the target location. Experiments show that the optimized ALV algorithm can effectively improve the overall accuracy of the natural landmarks and ensure the correspondence of the landmarks, so as to improve the accuracy of the ALV algorithm and make the robot reach the target location autonomously with a more ideal trajectory.