Robot SLAM and Navigation with Multi-Camera Computer Vision
Author(s)
Carrera Mendoza, Gerardo
Type
Thesis
Abstract
In this thesis we focus on computer vision capabilities suitable for practical mass-market
mobile robots, with an emphasis on techniques using rigs of multiple standard
cameras rather than more specialised sensors. We analyse the state of the art of service
robotics, and attempt to distill the vision capabilities which will be required of mobile
robots over the mid and long-term future to permit autonomous localisation, mapping
and navigation while integrating with other task-based vision requirements.
The first main novel contribution of the work is to consider how an ad-hoc multi-camera
rig can be used as the basis for metric navigation competences such as feature-based
Simultaneous Localisation and Mapping (SLAM). The key requirement for the
use of such techniques with multiple cameras is accurate calibration of the locations
of the cameras as mounted on the robot. This is a challenging problem, since we
consider the general case where the cameras might be mounted all around the robot
with arbitrary 3D locations and orientations, and may have fields of view which do not
intersect.
In the second main part of the thesis, we move away from the idea that all cameras
should contribute in a uniform manner to a single consistent metric representation,
inspired by recent work on SLAM systems which have demonstrated impressive
performance by a combination of off-the-shelf or simple techniques which we generally
categorise by the term ‘lightweight’. We develop a multi-camera mobile robot
vision system which goes beyond pure localisation and SLAM to permit fully autonomous
mapping navigation within a cluttered room, requiring free-space mapping
and obstacle-avoiding planning capabilities.
In the last part of the work we investigate the trade-offs involved in defining a
camera rig suitable for this type of vision system and perform some experiments on
camera placement.
mobile robots, with an emphasis on techniques using rigs of multiple standard
cameras rather than more specialised sensors. We analyse the state of the art of service
robotics, and attempt to distill the vision capabilities which will be required of mobile
robots over the mid and long-term future to permit autonomous localisation, mapping
and navigation while integrating with other task-based vision requirements.
The first main novel contribution of the work is to consider how an ad-hoc multi-camera
rig can be used as the basis for metric navigation competences such as feature-based
Simultaneous Localisation and Mapping (SLAM). The key requirement for the
use of such techniques with multiple cameras is accurate calibration of the locations
of the cameras as mounted on the robot. This is a challenging problem, since we
consider the general case where the cameras might be mounted all around the robot
with arbitrary 3D locations and orientations, and may have fields of view which do not
intersect.
In the second main part of the thesis, we move away from the idea that all cameras
should contribute in a uniform manner to a single consistent metric representation,
inspired by recent work on SLAM systems which have demonstrated impressive
performance by a combination of off-the-shelf or simple techniques which we generally
categorise by the term ‘lightweight’. We develop a multi-camera mobile robot
vision system which goes beyond pure localisation and SLAM to permit fully autonomous
mapping navigation within a cluttered room, requiring free-space mapping
and obstacle-avoiding planning capabilities.
In the last part of the work we investigate the trade-offs involved in defining a
camera rig suitable for this type of vision system and perform some experiments on
camera placement.
Date Issued
2012-03
Date Awarded
2012-05
Copyright Statement
Attribution NoDerivatives 4.0 International Licence (CC BY-ND)
Advisor
Davison, Andrew
Sponsor
Consejo Nacional de Ciencia y Tecnología (Mexico)
Publisher Department
Computing
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)