Comparison Between RGB and RGB-D Cameras for Supporting Low-Cost GNSS Urban Navigation

Abstract

A pure GNSS navigation is often unreliable in urban areas because of the presence of obstructions, thus preventing a correct reception of the satellite signal. The bridging between GNSS outages, as well as the vehicle attitude reconstruction, can be recovered by using complementary information, such as visual data acquired by RGB-D or RGB cameras. In this work, the possibility of integrating low-cost GNSS and visual data by means of an extended Kalman filter has been investigated. The focus is on the comparison between the use of RGB-D or RGB cameras. In particular, a Microsoft Kinect device (second generation) and a mirrorless Canon EOS M RGB camera have been compared. The former is an interesting RGB-D camera because of its low-cost, easiness of use and raw data accessibility. The latter has been selected for the high-quality of the acquired images and for the possibility of mounting fixed focal length lenses with a lower weight and cost with respect to a reflex camera. The designed extended Kalman filter takes as input the GNSS-only trajectory and the relative orientation between subsequent pairs of images. Depending on the visual data acquisition system, the filter is different because RGB-D cameras acquire both RGB and depth data, allowing to solve the scale problem, which is instead typical of image-only solutions. The two systems and filtering approaches were assessed by ad-hoc experimental tests, showing that the use of a Kinect device for supporting a u-blox low-cost receiver led to a trajectory with a decimeter accuracy, that is 15 % better than the one obtained when using the Canon EOS M camera.

GNSS/INS/LiDAR-SLAM Integrated Navigation System Based on Graph Optimization

“…In addition, for the low-cost MEMS (Micro-Electro-Mechanical System)-Inertial Measurement Unit (IMU), the navigation errors will accumulate and increase rapidly over time. In the context of low-cost integrated systems, image-based methodologies have also been explored, aiming at investigating the impact of drifts and errors experienced with such techniques, like in [2,3]. In the field of computer vision and robotics, Simultaneous Localization and Mapping (SLAM) technology is widely used for navigation in unfamiliar environments.…”