Fisheye-Based Method for GPS Localization Improvement in Unknown Semi-Obstructed Areas

Abstract

A precise GNSS (Global Navigation Satellite System) localization is vital for autonomous road vehicles, especially in cluttered or urban environments where satellites are occluded, preventing accurate positioning. We propose to fuse GPS (Global Positioning System) data with fisheye stereovision to face this problem independently to additional data, possibly outdated, unavailable, and needing correlation with reality. Our stereoscope is sky-facing with 360° × 180° fisheye cameras to observe surrounding obstacles. We propose a 3D modelling and plane extraction through following steps: stereoscope self-calibration for decalibration robustness, stereo matching considering neighbours epipolar curves to compute 3D, and robust plane fitting based on generated cartography and Hough transform. We use these 3D data with GPS raw data to estimate NLOS (Non Line Of Sight) reflected signals pseudorange delay. We exploit extracted planes to build a visibility mask for NLOS detection. A simplified 3D canyon model allows to compute reflections pseudorange delays. In the end, GPS positioning is computed considering corrected pseudoranges. With experimentations on real fixed scenes, we show generated 3D models reaching metric accuracy and improvement of horizontal GPS positioning accuracy by more than 50%. The proposed procedure is effective, and the proposed NLOS detection outperforms CN0-based methods (Carrier-to-receiver Noise density).

Precise Positioning of Uavs – Dealing With Challenging RTK-GPS Measurement Conditions During Automated Uav Flights

“…Primarily the signal quality check serves for the mitigation of NLOS reception, since these effects have the potential to cause limitless errors in the range measurements (Strode and Groves, 2015). Alternatively, the satellite visibility can be determined from camera systems that are aligned to the zenith direction or are equipped with fish-eye lenses (Lohmar, 1999;Meguro et al, 2009;Moreau et al, 2017). These techniques can be used to efficiently enhance navigation solutions in urban canyons and usually accuracies on the meter level are sufficient for tasks, such as distinguishing between pedestrian walkways and traffic lanes.…”

Skymask Matching Aided Positioning Using Sky-Pointing Fisheye Camera and 3D City Models in Urban Canyons

“…LiDAR is used to retrieve surrounding building or obstacle information, which in turn can be used to perform NLOS classification [ 13 ]. With the ability to obtain the building distance, the pseudorange can be corrected by the NLOS propagation model [ 14 ]. Sky-pointing fisheye cameras are capable of detecting obstacles and buildings in the local environment.…”

“…Sky-pointing fisheye cameras are capable of detecting obstacles and buildings in the local environment. When used in conjunction with image processing algorithms [ 2 , 4 , 14 , 15 , 16 , 17 ], they allow the exclusion of NLOS satellites from position calculations, improving positioning accuracy [ 15 ]. Furthermore, research indicates that if both LiDAR and fisheye cameras are used in conjunction, positioning accuracy in urban areas can be further enhanced [ 3 ].…”

A MEMS-IMU Assisted BDS Triple-Frequency Ambiguity Resolution Method in Complex Environments

“…The EWL observations formed by the linear combination of the triple-frequency original carrier phase observations have a wavelength of up to 5m, which can make the accuracy of the low-cost MEMS-IMU enough to meet the fixed ambiguity requirement. In addition, pseudoranges in complex environment such as urban canyon are affected by severe multipath effects, with large errors, sometimes as high as 4m [28]. It is difficult to fix the ambiguity, even the EWL ambiguity.…”