Panoramic SLAM from a multiple fisheye camera rig

This paper presents a feature-based simultaneous localization and mapping (SLAM) system for panoramic image sequences obtained from a multiple fisheye camera rig in a wide baseline mobile mapping system (MMS). First, the developed fisheye camera calibration method combines an equidistance projection model and trigonometric polynomial to achieve high-accuracy calibration from the fisheye camera to an equivalent ideal frame camera, which warrants an accurate transform from the fisheye images to a corresponding panoramic image. Second, we developed a panoramic camera model, corresponding bundle adjustment with a specific back propagation error function, and linear pose initialization algorithm. Third, the implemented feature-based SLAM pipeline consists of several specific strategies and algorithms in initialization, feature matching, frame tracking, and loop closing to overcome the difficulties in tracking wide baseline panoramic image sequences. We conducted experiments on large-scale MMS datasets of more than 15 km trajectories and 14,000 panoramic images as well as small-scale public video datasets. Our results show that the developed panoramic SLAM system PAN-SLAM can achieve fully-automatic camera localization and sparse map reconstruction in both small-scale indoor and large-scale outdoor environments including challenging scenes (e.g., dark tunnel) without the aid of any other sensors. The localization accuracy, which was measured by the absolute trajectory error (ATE), resembled the high-accuracy GNSS/INS reference of 0.1 m. PAN-SLAM also outperformed several feature-based fisheye and monocular SLAM systems with incomparable robustness in various environments. The system could be considered as a robust complementary solution and an alternative to expensive commercial navigation systems, especially in urban environments where signal obstruction and multipath interference are common. Source code and demo are available at http://study.rsgis.whu.edu.cn/pages/download/.