Visual Navigation in Modified Environments

From Biology to SLAM

Sotirios Ch. Diamantas and Richard Crowder

Outline

• Background Work– Biology– Models of Navigation– Snapshot and ALV Models– SLAM

• Implemented Work– Simulation Environment– Detection of Large-scale Landmarks– Recognition of Large-scale Landmarks– Localisation and Mapping with Minimal Sensing

• Current Work– Global and Local Vectors– Search Techniques

• Future Work

Background Work

Introduction

• Visual navigation is commonly used in today’s mobile robotic systems. Most work so far has focussed on unstructured unmodified environments. Modified environments raise in many situations for instance when an environment or area has been affected by some physical phenomena. The homing ability is an essential part of the navigation process where landmarks play a central role.

• Biology is seen as an alternative method to problems encountered by sophisticated robotic systems. Biological inspiration provides simple, yet effective solutions to those problems. The examination of biology has a twofold gain. The study of biology entails making better autonomous systems while at the same time the construction of such systems gives us an understanding of how the underlying mechanisms of a biological organism work.

Aims and Objectives

• The aim of this work is to apply vision systems to mobile robotics for the purpose of providing navigation capabilities to search and rescue robotic systems.

• The objective is to add to the understanding of visual-based navigation for fully autonomous mobile robots. Inspiration is drawn from biological and engineering studies, with the overriding objective of application with the search and rescue field, in modified environments.

• The purpose is not to make a complete model of a biological organism, but rather to take inspiration from as many biological mechanisms as possible. To complement the work, a study in visual homing has been conducted from the engineering point of view.

Landmarks and Modified Environments

• A landmark is a salient feature of the world. They can be divided into global and local, and natural or artificial.

• A modified environment can be characterised as one where the addition or removal of some objects alters the environment. This can be the case when rocks fall in a natural environment.

Biology• Insects (ants and honeybees) make use of :

– Path integration– Visual landmarks– Pheromone trail following– Searching techniques– Turn-back-and-look

approach during foraging

process where rate of change

of landmarks’ size is high

Models of Navigation

• Template hypothesis (Cartwright and Collett, 1983, 1987)• Parameter hypothesis (Anderson, 1977)

• Snapshot model• Average Landmark Vector (ALV) model (Lambrinos et al., 2000)

Snapshot Model

• Snapshot is an implementation of the template hypothesis. It requires a snapshot of the current and goal location. The compass direction is stored along with the snapshots. Image processing is required between current and goal position images.

ALV Model• ALV is a parsimonious model. It only requires a 2D vector to be stored with every

landmark. No matching and unwrapping of the image is required.

SLAM

• Simultaneous Localisation and Mapping (SLAM):– Laser Scan

• Landmark extraction (RANSAC)• Data Association

– Odometry• EKF odometry update• EKF re-observation• EKF new observations

Implemented Work

Simulation Environment• Vision sensors are cheap and provide a plethora of information. Other sensors, like

laser range scanners have been considered mainly for obstacle avoidance.• Simulation tools. 2D Player/Stage simulator and 3D Gazebo simulator.• Simulated devices and sensors:

– Mobile robotic platform

– Vision sensors

– Laser range finder

Detection of Large-scale Landmarks

where

if d < T then R is considered a homogeneous pixel

Detection of Large-scale Landmarks

• Different objects at varying intensities and illumination parameters

Detection of Large-scale Landmarks

Detection of Large-scale Landmarks

• Performance of algorithm at varying radii and image resolutions

Recognition of Large-scale Landmarks

• Sum of Absolute Differences (SAD)

• Normalised Cross Correlation (NCC)

Recognition of Large-scale Landmarks

• Correlation matrices of SAD and NCC algorithms.

Recognition of Large-scale Landmarks

• Correlation matrices of SAD and NCC algorithms at different camera perspective and illumination.

Recognition of Large-scale Landmarks

• Correlation matrices of SAD and NCC algorithms at perspectives with shadowy sides.

Localisation and Mapping with Minimal Sensing

• Use of laser range scanner to localise the robot and map the environment. No a-priori knowledge of the environment is required.

• Laser range data– RANSAC

– Expansion of obstacles using equation of circle and line

– Use quadratic equation

to detect laser rays that fall

within obstacle expansion

Localisation and Mapping with Minimal Sensing

• Select landmarks which are close to robot using Euclidean distance

• Use trigonometric functions to calculate x, y coordinates of the robot• Update robot location and map of the environment

Localisation and Mapping with Minimal Sensing

• Laser scans at different times using corner landmarks

Current Work

Global and Local Vectors

• Optic flow technique to infer angles between landmarks

• Attach vectors to landmarks

• Perform homing according to information available

• Navigation in the presence of obstacles, select wayout

• Use snapshots of Turn-Back-and-Look at the end of the process

Search Techniques• Perform systematic or random search techniques

– Perform spiral patterns found in nature

– Archimedean spiral

Future Work

• Fusion of different navigation strategies

• Use of visual and laser data to perform localisation and mapping, e.g., using Harris corner detector

• Use of SIFT features to solve data association problem

Questions

Thank you