GPS World, January 2017

ROBUST POSITIONING FROM VISUAL INERTIAL AND GPS Part of the drive trajectory in downtown Somerville JANUARY 2017 WWW GPSWORLD COM GPS WORLD 71 G PS positioning in urban scenarios is challenging because of large numbers of non line of sight outlier measurements We propose a robust positioning algorithm that combines GPS observations with visual inertial odometry information to handle such outliers We demonstrate the effectiveness of our algorithm in a simulation scenario with close to 80 outliers In experiments in a mild urban canyon environment our approach reduces the 95th percentile horizontal positioning error by 66 compared to a GPS only solution Motivation GPS performance drastically degrades if large parts of the sky are obstructed This occurs for example in urban canyon scenarios where GPS positions may be off by as much as 50 m These large positioning errors are prohibitive in applications such as autonomous vehicles and advanced driver assistance systems ADAS The large positioning errors in urban canyons are mainly caused by non line of sight NLOS observations and multipath effects Such observations result when the line of sight LOS path from the receiver to a satellite is blocked and the receiver instead erroneously tracks a reflected version of the satellite signal Summary of Results We propose a low cost method to detect and remove such NLOS outliers by combining GPS pseudorange measurements with visual inertial odometry VIO measurements These measurements are complementary GPS pseudoranges provide absolute positioning information VIO measurements constructed from camera frames and inertial measurements provide high accuracy relative positioning We develop a robust and efficient tightly coupled GPS VIO positioning algorithm able to work under extremely challenging conditions For example in scenarios with close to 80 of GPS measurement outliers or with only intermittent satellite visibility Even under these extreme conditions the proposed algorithms are able to produce reliable and accurate position estimates Problem Setting The overall positioning system consists of a GPS module and a VIO module The GPS module provides raw pseudorange and Doppler range rate measurements The VIO module consists of a camera along with inertial sensors such as an accelerometer and a gyroscope The output of the VIO processing engine are vectors of velocities and displacements expressed in the local camera coordinate frame We will not go into the details of the VIO design rather we will use it as a black box that provides us with the velocities The goal is to integrate the pseudorange measurements across time using the highly accurate velocities from the VIO to detect and discard the measurements corrupted by NLOS errors The positioning algorithm consists of two stages In the first stage we transform the velocities from the VIO frame of Part of the walk trajectory passing through a building where GPS is unavailable reference to the GPS frame of reference This requires estimation of the rotation matrix relating the VIO frame and the GPS frame Once this transformation is completed the second stage is to perform outlier detection and to estimate the rover position By Urs Niesen Venkatesan N Ekambaram Jubin Jose Lionel Garin and Xinzhou Wu Qualcomm Research Presented at ION GNSS September 2016