Robot Positioning Correction via Extended Kalman Filter Constraints
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Solution Overview
Problem
Existing robot positioning methods based on the extended Kalman filter algorithm suffer from prediction noise and measurement noise, leading to inaccuracies between predicted and actual robot positions.
Innovation Solution
A positioning method that uses an extended Kalman filter model to predict robot positions, incorporates sensor data, and corrects errors using a preset constraint condition to improve accuracy, involving steps like obtaining initial location information, predicting subsequent locations, and correcting for errors to obtain final location information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extended Kalman filter algorithm is used to predict robot position, then positioning accuracy is improved compared to traditional methods, but prediction noise and measurement noise cause errors between predicted and actual positions
Solution Approach 1:
The patent implements feedback by using the robot's actual movement information (from odometry and IMU sensors) to correct the predicted position in real-time. The correction unit compares the predicted position with actual position data and adjusts the predicted position accordingly, creating a closed-loop system that continuously refines positioning accuracy and reduces cumulative errors.
Solution Approach 2:
The patent introduces an intermediary correction mechanism that acts as a mediator between the predicted position (from extended Kalman filter) and the actual position (from sensors). The correction unit serves as this intermediary, processing both predicted and actual position data to generate a corrected position that combines the advantages of both sources while minimizing their respective errors.
2Measurement precision
If extended Kalman filter algorithm integrates multiple sensor data for positioning, then positioning accuracy is improved, but system complexity increases
Solution Approach 1:
The patent applies multi-functionality by using a single integrated positioning system that simultaneously processes data from multiple sensors (odometry, IMU, cameras) and performs multiple functions (prediction, correction, and final position determination) through the unified extended Kalman filter framework. This avoids the need for separate positioning subsystems and reduces overall system complexity.
Solution Approach 2:
The patent merges multiple sensor data streams (odometry information, IMU data, camera images) and multiple processing functions (prediction, correction, validation) into a single integrated positioning system. The correction unit combines predicted position, actual position, and constraint conditions into a unified corrected position, simplifying the system architecture while maintaining high accuracy.
3Measurement precision
If constraint conditions are applied to correct predicted position, then positioning accuracy is improved, but calculation complexity increases
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the constraint conditions based on the robot's current state and environment. The correction unit modifies the correction amount according to the specific constraint conditions (such as wall constraints, floor constraints, or object constraints), allowing the system to adapt to different scenarios without requiring completely different calculation frameworks, thus balancing accuracy and complexity.
Data Source
AI summary
The present disclosure relates to positioning technology, and particularly to a positioning method a positioning device, and a robot. In which, the method includes: obtaining first location information of the target object at a current moment being predicted by an extended Kalman filter model at a last moment; obtaining second location information of the target object at the current moment being collected by a sensor; predicting third location information of the target object at the current moment through the extended Kalman filter model based on the first location information and the second location information; and determining an error value of the third location information under a preset constraint condition, and correcting the third location information according, to the error value to obtain final location information of the target object at the current moment.