Vision-Aided Inertial Navigation

This document discloses, among other things, a system and method for implementing an algorithm to determine pose, velocity, acceleration or other navigation information using feature tracking data. The algorithm has computational complexity that is linear with the number of features tracked

Vision-aided inertial navigation with rolling-shutter cameras

Abstract In this paper, we focus on the problem of pose estimation using measurements from an inertial measurement unit and a rolling-shutter (RS

A Multi-State Constraint Kalman Filter for Vision-aided Inertial Navigation

In this paper, we present an Extended Kalman Filter (EKF)-based algorithm for real-time visionaided inertial navigation. The primary contribution of this work is the derivation of a measurement model that is able to express the geometric constraints that arise when a static feature is observed from multiple camera poses. This measurement model does not require including the 3D feature position in the state vector of the EKF and is optimal, up to linearization errors. The vision-aided inertial navigation algorithm we propose has computational complexity only linear in the number of features, and is capable of high-precision pose estimation in large-scale real-world environments. The performance of the algorithm is demonstrated in extensive experimental results, involving a camera/IMU system localizing within an urban area.