Robot Pose Estimation Using IMU and 2D Lidar Drift Correction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current robot pose estimation methods using inertial measurement units (IMUs) suffer from noise offsets, leading to integral drifts in position and velocity calculations, making accurate pose estimation impossible.
Innovation Solution
A method that combines IMU data with auxiliary sensors like GPS, barometers, magnetometers, and 2D lidar sensors for decoupling and correction, followed by a second correction using 3DoF pose data from the 2D lidar sensor to achieve higher precision in 6DoF pose estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If IMU sensors are used for robot pose estimation, then the robot can obtain angular velocity and displacement acceleration data, but the position and velocity obtained by integrating will have integral drifts due to sensor noise offset
Solution Approach 1:
The patent applies feedback by using the 2D lidar sensor to continuously measure the robot's actual pose and feeding this information back to correct the drift accumulated from IMU integration. The system compares the integrated pose with the measured pose and adjusts the estimation accordingly, eliminating the integral drift over time.
Solution Approach 2:
The 2D lidar sensor acts as an intermediary that provides independent pose measurement data to mediate between the IMU's acceleration/velocity data and the final position estimation. By introducing this intermediate measurement layer, the system can correct the cumulative errors from IMU integration without directly modifying the sensor hardware.
2Measurement precision
If multiple sensors are combined for pose estimation, then the accuracy can be improved, but the device complexity increases
Solution Approach 1:
The 2D lidar sensor serves multiple functions: it measures the robot's pose for drift correction, provides environmental mapping data, and can be used for navigation planning. This multi-functionality reduces the need for separate dedicated sensors for each function, thereby limiting the increase in device complexity while still achieving high measurement precision.
Data Source
AI summary
The present disclosure relates to robot technology, which provides a robot pose estimation method as well as an apparatus and a robot using the same. The method includes: obtaining, through an inertial measurement unit, initial 6DoF pose data; performing a first correction on the initial 6DoF pose data based on pose data obtained through an auxiliary sensor to obtain corrected 6DoF pose data; obtaining, through a 2D lidar sensor disposed on a stable platform, 3DoF pose data; and performing a second correction on the corrected 6DoF pose data based on the 3DoF pose data to obtain target 6DoF pose data. In this manner, the accuracy of the pose data of the robot is improved, and the accurate pose estimation of the robot is realized.