【摘要】：針對經(jīng)典單目實時定位與測圖(SLAM)采用卡爾曼濾波(EKF)濾波和FAST特征角點所存在的非線性誤差和魯棒性較差的問題,提出了一種改進的單目視覺實時定位與測圖方法。該方法采用相機中心的迭代EKF(IEKF)濾波方法,將特征點在當前相機坐標系下表達,并在線性化展開點附近迭代更新,不斷逼近最優(yōu)位置,從而最小化線性化誤差;針對特征點跟蹤的魯棒性、高效性及分布不均的問題,選用具有尺度和旋轉不變性,且探測和匹配效率更高的ORB特征作為特征角點,并采用一種由探測到篩選階段的整體網(wǎng)格化處理方法;另外,采用特征點逆深度參數(shù)化方法,避免了因深度信息未知而導致的局部地圖初始化錯誤問題,并采用1點隨機抽樣一致方法(RANSAC)濾波更新方法剔除錯誤的特征匹配,保證濾波估計的準確與穩(wěn)定。實驗采用外符合精度對算法進行評價,結果表明:新方法具有更強的魯棒性,絕對定位精度提升至2.24 m,誤差軌跡比提升至1.3%,且滿足實時性要求,是一種實用性較強的單目視覺實時定位與測圖方法。
[Abstract]:Aiming at the problem of poor nonlinear error and robustness of classical monocular real-time location and mapping (SLAM) using Kalman filter (EKF) filter and FAST feature corner, an improved monocular vision real-time location and mapping method is proposed. The method uses the camera center iterative EKF (IEKF) filtering method to express the feature points in the current camera coordinate system, and iteratively updates near the linearization expansion point to continuously approximate the optimal position, thus minimizing the linearization error. Aiming at the problem of robustness, high efficiency and uneven distribution of feature point tracking, ORB features with scale and rotation invariance and higher detection and matching efficiency are selected as feature corners. In addition, the method of inverse depth parameterization of feature points is used to avoid the problem of local map initialization error caused by unknown depth information. The one-point random sampling consistent method (RANSAC) filter updating method is used to eliminate the wrong feature matching to ensure the accuracy and stability of the filter estimation. The experimental results show that the new method is more robust, the absolute positioning accuracy is increased to 2.24 m, the error locus ratio is increased to 1.3%, and the real-time requirement is satisfied. It is a practical and practical method for real-time positioning and mapping of monocular vision.
【作者單位】： 信息工程大學導航與空天目標工程學院;
【基金】：國家自然科學基金(41274014,41501491)項目資助
【分類號】：TN713;TP391.41
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本文編號：2272644