An Analytical Measuring Rectification Algorithm of Monocular Systems in Dynamic Environment

Deshi Li,Xiaoliang Wang

doi:10.1155/2016/4132721

Abstract

Range estimation is crucial for maintaining a safe distance, in particular for vision navigation and localization. Monocular autonomous vehicles are appropriate for outdoor environment due to their mobility and operability. However, accurate range estimation using vision system is challenging because of the nonholonomic dynamics and susceptibility of vehicles. In this paper, a measuring rectification algorithm for range estimation under shaking conditions is designed. The proposed method focuses on how to estimate range using monocular vision when a shake occurs and the algorithm only requires the pose variations of the camera to be acquired. Simultaneously, it solves the problem of how to assimilate results from different kinds of sensors. To eliminate measuring errors by shakes, we establish a pose-range variation model. Afterwards, the algebraic relation between distance increment and a camera’s poses variation is formulated. The pose variations are presented in the form of roll, pitch, and yaw angle changes to evaluate the pixel coordinate incensement. To demonstrate the superiority of our proposed algorithm, the approach is validated in a laboratory environment using Pioneer 3-DX robots. The experimental results demonstrate that the proposed approach improves in the range accuracy significantly.

Highlights

The applications of mobile robots for observation and rescue missions have received an increasing attention in recent years
For mobile robots, retrieving their position is one of the important issues. To solve this problem, vision sensors have attracted a lot of attention because vision sensors are relatively inexpensive and compact with low power consumption
The range estimation algorithms using vision sensors are known as VO

Summary

Introduction

The applications of mobile robots for observation and rescue missions have received an increasing attention in recent years. The range estimation algorithms using vision sensors are known as VO (visual odometry). The visionbased method can solve both range and azimuth estimation problems using only the acquired image themselves. In the researches of intelligent unmanned vehicle systems, computer vision generally adopts the methods of imaging processing algorithms. In those works, the image features are extracted, along with the model of the ambient environment, for vehicle localization and obstacle avoidance. This paper concentrates on the dynamic measurement rectification problem in which the camera pose changes abruptly. This approach is suitable for applications such as navigating the autonomous vehicles running on rough terrains.

Related Works

Problem Formulation

Data Rectification Algorithm

Evaluations and Analysis

Findings

Conclusions