多镜头组合式相机的全景SLAM

同步定位与地图构建(simultaneous localization and mapping,SLAM)是摄影测量、计算机视觉和机器人学的研究热点,并广泛应用于移动测图系统、机器人、无人驾驶车等。本文提出一种针对多镜头组合式全景相机的基于特征的SLAM解决方案。首先,本文建立了鱼眼相机的高精度检校模型,以保证鱼眼相机与全景相机之间的高精度坐标转换;然后,将多镜头组合式全景成像模型嵌入SLAM的初始化、局部地图生成、关键帧选取、图优化等各个流程中。此外,考虑全景相机变形大、基线长的不利因素,本文在特征匹配、平差、特征点跟踪等SLAM的各个步骤都进行了针对性改进。本文在两套车载全景数据集共8000余张全景影像上进行试验。结果表明,本文所提出的全景SLAM很好地实现了全景相机的自动定位与地图构建功能,并达到了接近GPS参考的极高定位精度而无须借助GPS/IMU组合导航系统。相对于主流的基于平面相机的各类SLAM系统,如Mono-SLAM、Stereo-SLAM以及RGB-DSLAM,本文提出的全景SLAM可作为良好的补充,并为GPS信号失锁时的传感器定位提供廉价的自动解决方案。

Panoramic SLAM for multi-camera rig

Simultaneous localization and mapping (SLAM) is a research hotspot in fields of photogrammetry, computer vision and robotics, and has been widely applied in mobile mapping system, robots, driverless car, etc. This paper presents a fully automated feature based SLAM solution for a panoramic imaging system consisted of multi-camera rig. First, we developed a fisheye camera calibration model for guaranteeing high accurate coordinate transformation between the fisheye camera and the panoramic camera. Second, we imbedded the panoramic camera model into the SLAM process including initialization, local map building, key frame selection, graph optimization and bundle adjustment. In addition, we developed the algorithm in the processes of feature matching, bundle adjustment, frame tracking considering the disadvantages from the large image distortion and long baseline of the panoramic camera system. Experiments are executed on two data sets with more than 8000 panoramic images. Results show that the proposed panoramic SLAM solution achieves automatic camera localization and map construction, and the localization accuracy approaches the GPS reference. With respect to the mainstream SLAM systems based on conventional cameras, such as Mono-SLAM, Stereo-SLAM and RGB-D SLAM, our proposed panoramic SLAM system could serve as a beneficial supplement and supplies a cheap solution for GPS denied localization problem.