Improvement of Dead Reckoning in Urban Areas Through Integration of Low-Cost Multisensors

This paper presents a method of accurate dead reckoning in urban areas using low-cost sensors. In the evolution of advanced driver assistance systems, the seamless and accurate positioning of the vehicle has become one of the most important tasks, and dead reckoning plays an important role. Visual odometry is one of the most attractive approaches for this dead reckoning, but in urban areas, the accuracy of visual odometry is degraded due to the surrounding moving objects. Moreover, the error of heading estimation used with the visual odometry cannot be corrected with satellite information, due to poor satellite signal reception. In this study, solutions of these problems are presented to improve the accuracy of the visual odometry in urban environments. The first key technique is moving object detection using inertial measurement unite (IMU) and pattern recognition, which improves the robustness of visual odometry in the dynamic environments. The second key technique is heading estimation using time-series tightly coupled integration of satellite Doppler shift and IMU, which makes heading correction possible where there is poor satellite reception. In evaluation experiments in urban areas, the error of dead reckoning using this proposed method is reduced to about one-fourth compared to the conventional approach.