为减少动态环境对移动机器人同时定位与地图构建(simultaneous localization and mapping,SLAM)的影响,提出了一种特征点法视觉里程计自适应优化算法.该算法有助于改善光照条件变化情况下图像特征的不变性,有效提取纹理信息不充分区域的特征用于图像匹配.采用降采样法建立图像金字塔,将每个缩放后的图像根据预先设定规则划分为多个图像块.在每个图像块上进行光照非线性调整来增加图像细节,通过计算图像灰度概率分布来剔除无纹理区域.基于提出的方法建立了SLAM系统视觉里程计,并在TUM数据集上进行了验证.结果 表明:该算法可以减小移动机器人运动轨迹误差,改善机器人在不稳定动态环境下视觉里程计的性能.“,”Aiming to reduce the impact of dynamic environments on simultaneous localization and mapping (SLAM) of mobile robots,an adaptive optimization method in a feature-based visual odometry is proposed.The method helps to improve the invariance of image feature in illumination changing situation and to extract features effectively in areas where the texture information is not sufficient to make contributions to feature matching.Meanwhile,down sampling is applied to establish image pyramids and each scaled image is divided into cells based on a defined rule.Illumination adaptive nonlinear adjustments for each cell are applied to increase the image details,and low-texture area is removed by computing the image gray level probability distribution.Based on the proposed method,a visual odometry of SLAM system is built and verified on TUM dataset.The results show that,compared with the original system,the proposed method can reduce the trajectory errors of a mobile robot and also improve the performance of robot visual odometry in the unstable dynamic environments.