Vision Aided Inertial Navigation System

For aiding an inertial navigation system ins.

Vision aided inertial navigation system. We show that standard linearized estimation approaches such as the extended kalman filter ekf can fundamentally alter the system observability properties in terms of the number and structure of the unobservable directions. State of the art vision aided inertial navigation systems vins are able to provide highly accurate pose estimates over short periods of time however they continue to ex hibit limitations that prevent them from being used in critical applications for long term deployment. A vision aided ins vins employs camera observations of tracked features over multiple time steps for imposing geometric constraints between the motion of the vehicle and the. Many intelligent transportation system its applications will increasingly rely on lane level vehicle positioning that requires high accuracy bandwidth availability and integrity.

Most notably current approaches produce inconsistent state estimates. Sult vision aided inertial navigation systems are increasingly popular. A new approach to vision aided inertial navigation abstract. Introduction in the past few years the topic of vision aided inertial navigation has received considerable attention in the re search community.

In this paper we study estimator inconsistency in vision aided inertial navigation systems vins. In the future vision aided navigation systems could be integrated with wearable devices during tests in urban environments cerdec s soldier mounted prototype allowed the user to stay on nearly. In general a vision aided inertial navigation system vins fuses data from a camera and an inertial measurement unit imu to track the six degrees of freedom d o f position and orientation pose of a sensing platform. In this way the vins combines complementary sensing capabilities.

State of the art systems currently augment inertial measurements with visual odometry vo 1 19 21 23. This in turn allows the influx of spurious information leading to. Many robotic applications however require operation in gps denied areas e g indoors or within urban canyons. For example in 1 the information about the rotation and the direction of translation between two vehicles viewing a common scene is fused with imu measurements to estimate the relative transformation between two robots.

Incremental structure from motion with sparse bundle adjustment using a stereo camera provides real time highly. Recent advances in the manufacturing of mems based inertial sensors have made it possible to build small inexpensive and very accurate inertial. Alternatively vision aided inertial navigation methods have been proposed which utilize an imu in addition to a camera. Contributions in this paper we present a novel vision aided navigation system which is based on an imu and a stereo camera.