Egomotion
Encyclopedia
Egomotion is defined as the 3D motion of a camera within an environment. In the field of computer vision
, egomotion refers to estimating a camera's motion relative to a rigid scene. An example of egomotion estimation would be estimating a car's moving position relative to lines on the road or street signs as observed from the car itself. The estimation of egomotion is important in autonomous robot navigation applications.
techniques on a sequence of images captured by the moving camera. This is typically done using feature detection
to construct an optical flow
from two image frames in a sequence generated from either single cameras or stereo cameras. Using stereo image pairs for each frame helps reduce error and provides additional depth and scale information.
Features are detected in the first frame, and then matched in the second frame. This information is then used to make the optical flow field for the detected features in those two images. The optical flow field illustrates how features diverge from a single point, the focus of expansion. The focus of expansion can be detected from the optical flow field, indicating the direction of the motion of the camera, and thus providing an estimate of the camera motion.
There are other methods of extracting egomotion information from images as well, including a method that avoids feature detection and optical flow fields and directly uses the image intensities.
Computer vision
Computer vision is a field that includes methods for acquiring, processing, analysing, and understanding images and, in general, high-dimensional data from the real world in order to produce numerical or symbolic information, e.g., in the forms of decisions...
, egomotion refers to estimating a camera's motion relative to a rigid scene. An example of egomotion estimation would be estimating a car's moving position relative to lines on the road or street signs as observed from the car itself. The estimation of egomotion is important in autonomous robot navigation applications.
Overview
The goal of estimating the egomotion of a camera is to determine the 3D motion of that camera within the environment using a sequence of images taken by the camera. The process of estimating a camera's motion within an environment involves the use of visual odometryVisual odometry
In robotics and computer vision, visual odometry is the process of determining the position and orientation of a robot by analyzing the associated camera images...
techniques on a sequence of images captured by the moving camera. This is typically done using feature detection
Feature detection
In computer vision and image processing the concept of feature detection refers to methods that aim at computing abstractions of image information and making local decisions at every image point whether there is an image feature of a given type at that point or not...
to construct an optical flow
Optical flow
Optical flow or optic flow is the pattern of apparent motion of objects, surfaces, and edges in a visual scene caused by the relative motion between an observer and the scene. The concept of optical flow was first studied in the 1940s and ultimately published by American psychologist James J....
from two image frames in a sequence generated from either single cameras or stereo cameras. Using stereo image pairs for each frame helps reduce error and provides additional depth and scale information.
Features are detected in the first frame, and then matched in the second frame. This information is then used to make the optical flow field for the detected features in those two images. The optical flow field illustrates how features diverge from a single point, the focus of expansion. The focus of expansion can be detected from the optical flow field, indicating the direction of the motion of the camera, and thus providing an estimate of the camera motion.
There are other methods of extracting egomotion information from images as well, including a method that avoids feature detection and optical flow fields and directly uses the image intensities.