A photogrammetric approach for real-time visual SLAM applied to an omnidirectional system

The problem of sequential estimation of the exterior orientation of imaging sensors and the three-dimensional environment reconstruction in real time is commonly known as visual simultaneous localisation and mapping (vSLAM). Omnidirectional optical sensors have been increasingly used in vSLAM solutions, mainly for providing a wider view of the scene, allowing the extraction of more features. However, dealing with unmodelled points in the hyperhemispherical field poses challenges, mainly due to the complex lens geometry entailed in the image formation process. To address these challenges, the use of rigorous photogrammetric models that appropriately handle the geometry of fisheye lens cameras can overcome these challenges. Thus, this study presents a real-time vSLAM approach for omnidirectional systems adapting ORB-SLAM with a rigorous projection model (equisolid-angle). The implementation was conducted on the Nvidia Jetson TX2 board, and the approach was evaluated using hyperhemispherical images captured by a dual-fisheye camera (Ricoh Theta S) embedded into a mobile backpack platform. The trajectory covered a distance of 140 m, with the approach demonstrating accuracy better than 0.12 m at the beginning and achieving metre-level accuracy at the end of the trajectory. Additionally, we compared the performance of our proposed approach with a generic model for fisheye lens cameras. This study presents a real-time vSLAM for omnidirectional systems, adapting ORB-SLAM with a rigorous equisolid-angle projection model. Analysing a 140 m trajectory, it shows fewer discrepancies using hyperhemispherical images, outperforming a generic model like EUCM. Accuracy started at sub-0.12 m, reaching metre-level at the end.image