A Visual Odometry for Wide Angle Fovea Sensor SLAM - Locally-High Accurate and Wide-Angle Mapping Method -
A Visual Odometry for Wide Angle Fovea Sensor SLAM - Locally-High Accurate and Wide-Angle Mapping Method -
复制标题
用于广角中央凹传感器 SLAM 的视觉里程计 - 局部高精度和广角建图方法 -
DOI：
10.1109/iecon48115.2021.9589175
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Mastrogiovanni Fulvio
中科院分区：
文献类型：
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作者：
Takamura Tomoki;Shimizu Sota;Murakami Rei;Carfi Alessandro;Mastrogiovanni Fulvio
提出了一种用于广角中心凹传感器SLAM（WAF-SLAM）的广角中心凹视觉里程计（WAF-VO）方法，该方法在进行摄像机运动估计的同时，还能生成局部高精度的广角地图。WAF传感器是一种特制的广角传感器，其灵感来自人类视觉功能，即，图像的空间分辨率在整个视场（FOV）中是不均匀的;它在中心FOV中高得多，并且朝向外围快速降低。我们的视觉里程计方法的强烈特点是从WAF传感器的输入图像的广角FOV和空间变化的分辨率。提出了一种局部高精度广角映射方法，作为WAF-SLAM的主要组成部分，与摄像机运动估计一起。我们提出的方法估计相机运动更稳定地使用非常低的空间分辨率的广角图像从WAF传感器的输入图像重新映射。使用估计的相机运动，窄角高精度地图生成从输入图像的高空间分辨率中心区域中的对应特征点。广角图是从除了上述极低空间分辨率图像之外的从输入图像重新映射的中等空间分辨率广角图像中的广角图生成的。当生成广角图时，通过根据FOV的区域调整SIFT特征的对比度阈值来增加提取的特征点的数量。提出了一种利用极线约束改进的KNN匹配方法，避免了增加的特征点的误匹配。因此，从更正确的对应特征点生成广角图。最后，将上述两种地图组合成唯一的局部高精度广角地图，即，WAF地图。使用我们提出的方法，WAF地图生成的验证实验。此外，本文提出了一个评估的准确性和精度的生成地图。
This paper presents a method of wide angle fovea visual odometry (WAF-VO) for Wide Angle Fovea Sensor SLAM (WAF-SLAM), by which a unique locally-high accurate and wide-angle map is generated in addition to camera motion estimation. The WAF sensor is a special-made wide-angle sensor that is inspired from human visual function, i.e., the spatial resolution of the image is not uniform throughout the entire field of view (FOV); it is much higher in the central FOV and decreases rapidly towards the periphery. Our visual odometry method is strongly characterized by a wide-angle FOV and space-variant resolution of the input image from the WAF sensor. A locally-high accurate and wide-angle mapping method is proposed as a major part for WAF-SLAM together with the camera motion estimation. Our proposed method estimates camera motions more stably using very low-spatial-resolution wide-angle images remapped from the input image of the WAF sensor. Using the estimated camera motions, narrow-angle high accurate maps are generated from corresponding feature points in high-spatial resolution central regions of the input image. Wide-angle maps are generated from ones in middle-spatial-resolution wide-angle images remapped from the input image apart from the above very low-spatial-resolution images. When the wide-angle maps are generated, the number of extracted feature points is increased by adjusting contrast threshold values of SIFT feature according to regions of the FOV. A KNN matching method improved using epipolar constraint is proposed and employed for avoidance of mismatching the increased feature points. Thus, the wide-angle maps are generated from more correct corresponding feature points. Finally, the above two types of maps are combined into the unique locally-high accurate and wide-angle map, i.e., a WAF map. Using our proposed method, the WAF map was generated by verification experiments. Furthermore, the paper presents an evaluation of the accuracy and precision of the generated map.