Multi-Sensor Robot Navigation for LiDAR Distortion Correction
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Solution Overview
Problem
Conventional autonomous mobile robots (AMRs) face challenges in accurate positioning due to motion distortion of LiDAR data and sparse sampling, leading to gravity vector drift and elevation estimation errors, especially in complex environments.
Innovation Solution
A multi-sensor-fusion-based navigation method that acquires inertial measurement data and three-dimensional point cloud data, performs distortion correction, and matches the corrected data with navigation maps using GPS information to determine the robot's position, integrating inertial measurement data, LiDAR data, and GPS data for accurate positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If LiDAR is used for SLAM positioning, then the robot can navigate autonomously, but motion distortion causes positioning errors and gravity vector drift
Solution Approach 1:
The patent combines LiDAR, IMU, and GPS sensors into a unified navigation system. The LiDAR provides environmental mapping, the IMU compensates for motion distortion through inertial measurement, and GPS provides absolute position reference, together resolving the positioning accuracy issue while maintaining autonomous navigation
Solution Approach 2:
The IMU acts as an intermediary that compensates for the motion distortion between LiDAR scans. By measuring the robot's motion state and correcting the LiDAR point cloud data accordingly, the system eliminates gravity vector drift and positioning errors caused by motion during scanning
2Device complexity
If LiDAR sampling is performed at low frequency, then the system is simpler, but sparse sampling leads to elevation estimation errors
Solution Approach 1:
The patent changes the sampling parameters by fusing IMU data with LiDAR data. The IMU provides high-frequency motion information that compensates for the low sampling rate of LiDAR, allowing accurate elevation estimation without increasing LiDAR sampling frequency or system complexity
Solution Approach 2:
The patent replaces the need for high-frequency LiDAR sampling with a computational approach using IMU data. Instead of mechanically increasing the sampling rate, the system uses inertial measurement and distortion correction algorithms to achieve accurate elevation estimation at lower sampling rates
3Measurement precision
If radar matching is used in simple environments, then positioning can be achieved, but the method diverges in complex environments and cannot achieve accurate positioning
Solution Approach 1:
The patent creates a universal positioning system that works across different environments by combining multiple sensing modalities. The LiDAR provides structure-aware mapping that adapts to both simple and complex environments, while IMU and GPS provide environment-independent reference information, making the system versatile across varying environmental conditions
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method effectively corrects positioning errors caused by motion distortion, enabling accurate and reliable navigation of AMRs in both indoor and outdoor environments by combining inertial and LiDAR data with GPS information.
Implementation Method 1
acquiring inertial measurement data and three-dimensional point cloud data of a robot at a current position; determining a pose change of the robot based on the inertial measurement data of the robot at the current position
Implementation Method 2
acquiring inertial measurement data and three-dimensional point cloud data of a robot at a current position
Implementation Method 3
acquiring GPS information of the robot at the current position
Implementation Method 4
performing distortion correction on the three-dimensional point cloud data of the robot at the current position based on the pose change of the robot
Data Source
AI summary
The present application relates to a multi-sensor-fusion-based autonomous mobile robot indoor and outdoor navigation method and a robot. The method includes: acquiring inertial measurement data and three-dimensional point cloud data of a robot at a current position; determining a pose change of the robot based on the inertial measurement data of the robot at the current position; performing distortion correction on the three-dimensional point cloud data of the robot at the current position based on the pose change of the robot; and matching the three-dimensional point cloud data after the distortion correction with a navigation map, to determine the current position of the robot. With the method, the robot can be accurately positioned.