Walking Robot SLAM Using Odometry and Image Fusion
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Solution Overview
Problem
Existing walking robots face challenges in accurately estimating their position using image data from cameras due to movement and vibration, which disrupts the image data captured, making it difficult to achieve precise localization and mapping in dynamic environments.
Innovation Solution
The implementation of odometry data, acquired through kinematic and rotational angle data, is integrated with image-based SLAM technology to improve the accuracy and convergence of localization, and inertial data is fused with odometry data to enhance the precision of position estimation and mapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If image-based SLAM technology is used for localization, then the robot can operate without artificial landmarks, but the movement and vibration of the walking robot disrupt the image data captured by cameras, making accurate position estimation difficult
Solution Approach 1:
The patent combines image-based SLAM with odometry technology by integrating camera data with motion data from rotary joint sensors. The control unit fuses these multiple data sources to calculate position information, thereby compensating for the disturbances caused by robot movement and vibration that affect pure image-based methods.
Solution Approach 2:
The patent introduces odometry data as an intermediary element between the camera system and the localization process. The odometry data, derived from rotary joint angles and link lengths, serves as a mediator that compensates for the instability in image data caused by robot movement, enabling more accurate position estimation.
2Device complexity
If only image data from cameras is used for SLAM, then the system remains simple, but the vibration and movement of the walking robot function as disturbances that substantially prevent accurate position estimation
Solution Approach 1:
The patent merges the simple image-based SLAM system with odometry measurement components. By combining camera data with motion data from rotary joints and link length information, the system maintains relative simplicity while significantly improving localization reliability through data fusion in the control unit.
3Measurement precision
If artificial landmarks are installed to recognize position, then absolute position data can be obtained when landmarks are detected, but the burden of landmark installation increases when the space size is increased, and position data are not obtained when landmark detection fails
Solution Approach 1:
The patent extracts the dependency on artificial landmarks by implementing natural feature-based SLAM combined with odometry. The system uses natural features in the environment and motion data from rotary joints to determine position, thereby removing the need for artificial landmark installation while maintaining position estimation capability.
Solution Approach 2:
The patent enables the robot to self-determine its position through onboard sensors (cameras and rotary joint angle sensors) and computational processing in the control unit. The system uses its own motion data and environmental features to autonomously calculate position information without requiring external landmark infrastructure.
Data Source
AI summary
A walking robot and a simultaneous localization and mapping method thereof in which odometry data acquired during movement of the walking robot are applied to image-based SLAM technology so as to improve accuracy and convergence of localization of the walking robot. The simultaneous localization and mapping method includes acquiring image data of a space about which the walking robot walks and rotational angle data of rotary joints relating to walking of the walking robot, calculating odometry data using kinematic data of respective links constituting the walking robot and the rotational angle data, and localizing the walking robot and mapping the space about which the walking robot walks using the image data and the odometry data.