In April 2016 Manchester eScholar was replaced by the University of Manchester’s new Research Information Management System, Pure. In the autumn the University’s research outputs will be available to search and browse via a new Research Portal. Until then the University’s full publication record can be accessed via a temporary portal and the old eScholar content is available to search and browse via this archive.

QUANTITATIVE PERFORMANCE EVALUATION OF AUTONOMOUS VISUAL NAVIGATION

Tian, Jingduo

[Thesis]. Manchester, UK: The University of Manchester; 2017.

Access to files

Abstract

Autonomous visual navigation algorithms for ground mobile robotic systems working in unstructured environments have been extensively studied for decades. Among these work, algorithm performance evaluations between different design configurations mainly involve the use of benchmark datasets with a limited number of real-world trails. Such evaluations, however, have difficulties to provide sufficient statistical power for performance quantification. In addition, they are unable to independently assess the algorithm robustness to individual realistic uncertainty sources, including the environment variations and processing errors. This research presents a quantitative approach to performance and robustness evaluation and optimisation of autonomous visual navigation algorithms, using large scale Monte-Carlo analyses. The Monte-Carlo analyses are supported by a simulation environment designed to represent a real-world level of visual information, using the perturbations from realistic visual uncertainties and processing errors. With the proposed evaluation method, a stereo vision based autonomous visual navigation algorithm is designed and iteratively optimised. This algorithm encodes edge-based 3D patterns into a topological map, and use them for the subsequent global localisation and navigation. An evaluation on the performance perturbations from individual uncertainty sources indicates that the stereo match error produces significant limitation for the current system design. Therefore, an optimisation approach is proposed to mitigate such an error. This maximises the Fisher information available in stereo image pairs by manipulating the stereo geometry. Moreover, the simulation environment is further updated in association with the algorithm design, which include the quantitative modelling and simulation of localisation error to the subsequent navigation behaviour. During a long-term Monte-Carlo evaluation and optimisation, the algorithm performance has been significantly improved. Simulation experiments demonstrate that the navigation of a 3-DoF robotic system is achieved in an unstructured environment, while possessing sufficient robustness to realistic visual uncertainty sources and systematic processing errors.

Bibliographic metadata

Type of resource:
Content type:
Form of thesis:
Type of submission:
Degree type:
Doctor of Philosophy
Degree programme:
PhD Electrical and Electronic Engineering
Publication date:
Location:
Manchester, UK
Total pages:
208
Abstract:
Autonomous visual navigation algorithms for ground mobile robotic systems working in unstructured environments have been extensively studied for decades. Among these work, algorithm performance evaluations between different design configurations mainly involve the use of benchmark datasets with a limited number of real-world trails. Such evaluations, however, have difficulties to provide sufficient statistical power for performance quantification. In addition, they are unable to independently assess the algorithm robustness to individual realistic uncertainty sources, including the environment variations and processing errors. This research presents a quantitative approach to performance and robustness evaluation and optimisation of autonomous visual navigation algorithms, using large scale Monte-Carlo analyses. The Monte-Carlo analyses are supported by a simulation environment designed to represent a real-world level of visual information, using the perturbations from realistic visual uncertainties and processing errors. With the proposed evaluation method, a stereo vision based autonomous visual navigation algorithm is designed and iteratively optimised. This algorithm encodes edge-based 3D patterns into a topological map, and use them for the subsequent global localisation and navigation. An evaluation on the performance perturbations from individual uncertainty sources indicates that the stereo match error produces significant limitation for the current system design. Therefore, an optimisation approach is proposed to mitigate such an error. This maximises the Fisher information available in stereo image pairs by manipulating the stereo geometry. Moreover, the simulation environment is further updated in association with the algorithm design, which include the quantitative modelling and simulation of localisation error to the subsequent navigation behaviour. During a long-term Monte-Carlo evaluation and optimisation, the algorithm performance has been significantly improved. Simulation experiments demonstrate that the navigation of a 3-DoF robotic system is achieved in an unstructured environment, while possessing sufficient robustness to realistic visual uncertainty sources and systematic processing errors.
Thesis main supervisor(s):
Language:
en

Record metadata

Manchester eScholar ID:
uk-ac-man-scw:312243
Created by:
Tian, Jingduo
Created:
21st November, 2017, 12:43:57
Last modified by:
Tian, Jingduo
Last modified:
13th September, 2018, 13:51:35

Can we help?

The library chat service will be available from 11am-3pm Monday to Friday (excluding Bank Holidays). You can also email your enquiry to us.