【摘要】 基于子图分割和自适应噪音方差的2D移动机器人定位方法,不仅能有效地检测闭环,而且能更精准地估计移动机器人的位姿。首先,子图分割能够有效提高移动机器人的定位效率,通过匹配局部子图也能提高闭环检测的准确性,减少测量噪音的影响。与之前工作不同的是,根据2D几何特征点的个数来分割子图,使得子图中有足够的特征点,进而提高闭环检测的准确性。其次,在利用unscented卡尔曼滤波(UKF)模型时,使用自适应的噪音方差来估计移动机器人运动路径,使得每次UKF的预测方差与移动机器人当前环境有关,当检测到闭环时,通过UKF融合闭环定位信息,可以更准确地估计机器人位姿。在实验中,首先使用两组经典的移动机器人地图数据来比较基于特征点分割子图的方法与基于帧数分割子图的方法在闭环检测时的准确性;然后使用真实的移动智能车在室内环境进行实验,证明了自适应方差比常量方差有更高的定位精度。更多还原

【Abstract】 Submap and adaptive covariance based 2DSLAM solution can not only achieve efficient loop-closure detection but also accurate localization.Firstly,the loop-closure is detected by efficiently matching 2Dgeometric features between local submaps.Unlike the previous methods which often use the number of the measure frames as the criteria of the division,we employed the number of features as the main criteria.To achieve accurate localization,we proposed an adaptive Kalman filter to estimate the final pose.Moreover,the prediction and observation covariance are adaptive and estimated by the scan-matching algorithm.Finally,if a loop-closure is detected,the optimized transformation and covariance from the backend can be fused directly in the Kalman filter.In the first experiment,the comparison between the two kinds of submap division mechanism verifies the validity of the proposed method.The second experiment shows that the proposed method can accurately localize the robot only using a single lidar.更多还原