Vision Aided Inertial Navigation System

State of the art systems currently augment inertial measurements with visual odometry vo 1 19 21 23.

Vision aided inertial navigation system. Contributions in this paper we present a novel vision aided navigation system which is based on an imu and a stereo camera. Incremental structure from motion with sparse bundle adjustment using a stereo camera provides real time highly. Many robotic applications however require operation in gps denied areas e g indoors or within urban canyons. We combine a visual odometry system with an aided inertial navigation filter to produce a precise and robust navigation system that does not rely on external infrastructure.

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. This in turn allows the influx of spurious information leading to. In this way the vins combines complementary sensing capabilities. Real time computer vision dgps aided inertial navigation system for lane level vehicle navigation abstract.

Many intelligent transportation system its applications will increasingly rely on lane level vehicle positioning that requires high accuracy bandwidth availability and integrity. 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. Introduction in the past few years the topic of vision aided inertial navigation has received considerable attention in the re search community. 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.

A new approach to vision aided inertial navigation abstract. Camera imu system localizing within an urban area. In this paper we study estimator inconsistency in vision aided inertial navigation systems vins. Alternatively vision aided inertial navigation methods have been proposed which utilize an imu in addition to a camera.

Most notably current approaches produce inconsistent state estimates. 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. Recent advances in the manufacturing of mems based inertial sensors have made it possible to build small inexpensive and very accurate inertial.