A square root unscented Kalman filter for multiple view geometry based stereo cameras/inertial navigation

Zhiwen Xian,Mao Shan,Junxiang Lian,Xiaoping Hu,Xiaofeng He,Lilian Zhang

doi:10.1177/1729881416664850

Abstract

Exact motion estimation is one of the major tasks in autonomous navigation. Conventional Global Positioning System-aided inertial navigation systems are able to provide accurate locations. However, they are limited when used in a Global Positioning System-denied environment. In this paper, we present a square root unscented Kalman filter-based approach for navigation by using stereo cameras and an inertial sensor only. The main contribution of this work is the development of a novel measurement model by applying multiple view geometry constraints to the stereo cameras/inertial system. The measurement model does not require the three-dimensional feature position in the state vector of the filter, which substantially reduces the size of the state vector and the computational burden. To incorporate this nonlinear and complex measurement model, a variant of the square root unscented Kalman filter-based algorithm is also proposed. The root of the state covariance is propagated and updated directly in the square root unscented Kalman filter, thereby avoiding the decomposition of the state covariance and improving the stability of our algorithm. Experimental results based on a real outdoor dataset are presented to demonstrate the feasibility and the accuracy of the proposed approach.

Highlights

In recent years, the inertial measurement unit (IMU) has been widely used for the navigation tasks of robots, cars, unmanned aerial vehicles, and so forth, thanks to the remarkable advances in Micro-Electro-Mechanical System (MEMS) inertial sensors, such as lower cost, smaller size, lighter weight, higher power efficiency, etc
Since we focus on navigation tasks based on stereo and inertial sensors, we used the unsynced IMU measurements sampling at 100 Hz and the rectified grayscale stereo sequences sampling at 10 Hz
We present an square root UKF (SRUKF)-based stereo camera and inertial sensor integration architecture for autonomous navigation

Summary

Introduction

The inertial measurement unit (IMU) has been widely used for the navigation tasks of robots, cars, unmanned aerial vehicles, and so forth, thanks to the remarkable advances in Micro-Electro-Mechanical System (MEMS) inertial sensors, such as lower cost, smaller size, lighter weight, higher power efficiency, etc. The IMUbased inertial navigation system (INS) is able to track fast motion with high precision in a short period of time. The accuracy of INS deteriorates with time because of the accumulative error caused by sensor biases and noises. A conventional method to tackle this problem is using the Global Positioning System (GPS) to correct the error of INS periodically. A major limitation of the approach is that the GPS/INS system cannot be used in GPS-denied environments where GPS receivers do not function,[1] such as indoors, forests, and underground. High precision GPS receivers are usually expensive and bulky

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