Omni-directional Camera Visual Odometry for Mobile Robot Localization
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Solution Overview
Problem
Mobile robots face challenges in accurate localization and map building in unknown environments due to noise in infrared sensor data and errors from wheel slippage or spin, which affect odometry calculations.
Innovation Solution
A method involving an omni-directional camera installed parallel to the horizontal plane of a mobile robot, dividing captured images into upper and lower sections, extracting feature points, calculating visual odometry information, and using an Extended Kalman Filter for simultaneous localization and map building.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If infrared sensor data is used for localization and map building, then the robot can navigate in unknown environments, but the noise in the sensor data causes difficulty in calculating the location accurately
Solution Approach 1:
The patent introduces visual sensors (cameras) as an intermediary to capture images of the environment, which are then processed to extract feature points and calculate odometry information. This intermediary visual information serves as a mediator between the robot's movement and the localization system, providing more reliable data than infrared sensors alone for calculating location in unknown environments
2Area of stationary object
If the robot moves to explore the environment for map building, then the map coverage increases, but errors in robot location occur due to wheel slippage and radius variation
Solution Approach 1:
The patent implements a feedback mechanism where the robot captures images during movement, extracts feature points from these images, and uses visual odometry to calculate actual displacement. This visual feedback is then used to correct odometry errors accumulated from wheel slippage and radius variation, continuously refining the location tracking reliability as the robot explores and expands the map coverage
Solution Approach 2:
The patent replaces reliance on the mechanical odometry system (wheel-based distance measurement) with a visual-based odometry system. By substituting the mechanical measurement method with optical image processing and feature point tracking, the system eliminates errors caused by wheel slippage and radius variation, maintaining reliable location tracking even as the robot moves extensively to build comprehensive maps
3Measurement precision
If visual sensors are used to correct odometry errors, then location accuracy improves, but the complexity of the system increases
Solution Approach 1:
The patent makes the visual sensor system multi-functional by using the same camera and image processing pipeline for both environment mapping and odometry calculation. The feature point extraction and image processing algorithms serve dual purposes: building the environmental map and correcting location errors, thereby improving location accuracy without proportionally increasing system complexity
Data Source
AI summary
A simultaneous localization and map building method of a mobile robot including an omni-directional camera. The method includes acquiring an omni-directional image from the omni-directional camera, dividing the obtained omni-directional image into upper and lower images according to a preset reference to generate a first image, which is the lower image, and a second image, which is the upper image, extracting feature points from the first image and calculating visual odometry information calculating visual odometry information to track locations of the extracted feature points based on a location of the omni-directional camera, and performing localization and map building of the mobile robot using the calculated visual odometry information and the second image as an input of an extended Kalman filter.