Visual Odometry in a 2-D environment

CS-365A Course Project

BY:Aakriti Mittal (12005)Keerti Anand (13344)

Under the guidance of:Prof. Amitabha Mukherjee

Introduction

Visual Odometry is the process of estimating the position and orientation of the robot using the camera images associated with it.

Our aim is to determine the location of the robotic arm, having 2 or 3 links with camera mounted on the top of the end effector, by analysing the images of the environment of the robot.

Motivation

Mars Land Rover Mission All those places where GPS can’t be used to

determine one’s location Visual Odometry provides a quite accurate tool to

handle the problem of navigation and finding its location

Approach

Environment: Square shaped box with 4 coloured walls and black corners

Approach Robot: Robotic arm kept at the centre of the box with 2 (or 3)

links and a camera at the end effector. Camera will have 180 field of view and it will emit rays at an angular difference of 1

Approach Images: For different robot configurations (different values of

we will obtain the images dataset. This will be used to train the robot, so that when it encounters a new image, it can determine its configuration.

Approach

Using Isomap Algorithm map the image dataset to 2-D or 3-D space

Using neural network, map it to the configuration space ()

References R. Horaud, R. Mohr, F. Dornaika, and B. Boufama, The advantage

of mounting a camera onto a robot arm, in In Proc. of the Europe-China Workshop on Geometrical Modelling and Invariants for Computer Vision, pp. 206-213.

D. Nister, O. Naroditsky, and J. Bergen, Visual odometry, in Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on, vol. 1, IEEE, 2004, pp. I-652.

Visual odometry for ground vehicle applications, Journal of Field Robotics, 23 (2006), pp. 3-20.

D. Scaramuzza and F. Fraundorfer, Visual odometry [tutorial], Robotics & Automation Magazine, IEEE, 18 (2011), pp. 80-92.

Swati, Construction of ego-model of robot arm. http://home.iitk.ac.in/~swatim/cs365/project/report.pdf, 2013.

Thank you!!

Questions??