The extraction and description of image features are very important for visual simultaneous localization and mapping(V-SLAM).A rotated boosted efficient binary local image descriptor(BEBLID)SLAM(RB-SLAM)algorithm base...The extraction and description of image features are very important for visual simultaneous localization and mapping(V-SLAM).A rotated boosted efficient binary local image descriptor(BEBLID)SLAM(RB-SLAM)algorithm based on improved oriented fast and rotated brief(ORB)feature description is proposed in this paper,which can solve the problems of low localization accuracy and time efficiency of the current ORB-SLAM3 algorithm.Firstly,it uses the BEBLID to replace the feature point description algorithm of the original ORB to enhance the expressiveness and description efficiency of the image.Secondly,it adds rotational invariance to the BEBLID using the orientation information of the feature points.It also selects the rotationally stable bits in the BEBLID to further enhance the rotational invariance of the BEBLID.Finally,it retrains the binary visual dictionary based on the BEBLID to reduce the cumulative error of V-SLAM and improve the loading speed of the visual dictionary.Experiments show that the dictionary loading efficiency is improved by more than 10 times.The RB-SLAM algorithm improves the trajectory accuracy by 24.75%on the TUM dataset and 26.25%on the EuRoC dataset compared to the ORB-SLAM3 algorithm.展开更多
针对小行星接近撞击任务的自主导航需求,提出了一种基于图像配准的暗弱目标小行星识别方法。小行星尺寸小、亮度弱(一般10星等以上),导航敏感器需要具备暗弱目标成像能力以实现小行星成像。这导致导航敏感器会同时拍摄到大量未知的暗弱...针对小行星接近撞击任务的自主导航需求,提出了一种基于图像配准的暗弱目标小行星识别方法。小行星尺寸小、亮度弱(一般10星等以上),导航敏感器需要具备暗弱目标成像能力以实现小行星成像。这导致导航敏感器会同时拍摄到大量未知的暗弱恒星,给目标小行星的精确识别带来挑战。利用小行星与背景恒星的相对运动,首先采取改进的具有旋转不变性的FAST和BRIEF算法(Oriented FAST and Rotated BRIEF,ORB)特征点定位与增强的高效局部图像特征描述符(Boosted Efficient Binary Local Image Descriptor,BEBLID)特征点描述结合的方法对帧间图像进行配准,再基于阈值分割法识别星点,逐星点计算相应窗口之间的结构相似性指数,最后完成目标小行星的检测。该方法的速度和精度相比传统的图像配准方法和目标小行星检测方法有较大提升,克服了目标小行星暗弱、存在未知背景恒星的问题,为小行星防御的光学自主导航提供视线矢量等信息。展开更多
文摘The extraction and description of image features are very important for visual simultaneous localization and mapping(V-SLAM).A rotated boosted efficient binary local image descriptor(BEBLID)SLAM(RB-SLAM)algorithm based on improved oriented fast and rotated brief(ORB)feature description is proposed in this paper,which can solve the problems of low localization accuracy and time efficiency of the current ORB-SLAM3 algorithm.Firstly,it uses the BEBLID to replace the feature point description algorithm of the original ORB to enhance the expressiveness and description efficiency of the image.Secondly,it adds rotational invariance to the BEBLID using the orientation information of the feature points.It also selects the rotationally stable bits in the BEBLID to further enhance the rotational invariance of the BEBLID.Finally,it retrains the binary visual dictionary based on the BEBLID to reduce the cumulative error of V-SLAM and improve the loading speed of the visual dictionary.Experiments show that the dictionary loading efficiency is improved by more than 10 times.The RB-SLAM algorithm improves the trajectory accuracy by 24.75%on the TUM dataset and 26.25%on the EuRoC dataset compared to the ORB-SLAM3 algorithm.
文摘针对小行星接近撞击任务的自主导航需求,提出了一种基于图像配准的暗弱目标小行星识别方法。小行星尺寸小、亮度弱(一般10星等以上),导航敏感器需要具备暗弱目标成像能力以实现小行星成像。这导致导航敏感器会同时拍摄到大量未知的暗弱恒星,给目标小行星的精确识别带来挑战。利用小行星与背景恒星的相对运动,首先采取改进的具有旋转不变性的FAST和BRIEF算法(Oriented FAST and Rotated BRIEF,ORB)特征点定位与增强的高效局部图像特征描述符(Boosted Efficient Binary Local Image Descriptor,BEBLID)特征点描述结合的方法对帧间图像进行配准,再基于阈值分割法识别星点,逐星点计算相应窗口之间的结构相似性指数,最后完成目标小行星的检测。该方法的速度和精度相比传统的图像配准方法和目标小行星检测方法有较大提升,克服了目标小行星暗弱、存在未知背景恒星的问题,为小行星防御的光学自主导航提供视线矢量等信息。
