FMCW Lidar Sensor Alignment Using IMU Feedback
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Solution Overview
Problem
Lidar sensors in vehicles can become misaligned due to vibrations and shocks during operation, affecting the accuracy of sensor fusion and vehicle navigation, especially when combining point cloud data with other sensor modalities like video and radar.
Innovation Solution
The method involves using inertial measurement unit (IMU) data to determine lidar sensor misalignment in six degrees of freedom, aligning the lidar sensor with the vehicle's real-world coordinate system, and employing neural networks to filter velocity lidar point cloud data and apply Savitzky-Golay filtering to smooth static data points, allowing for accurate alignment and operation based on aligned sensor data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If lidar sensor is initially aligned when vehicle is manufactured, then alignment accuracy is maintained at first, but lidar sensor becomes misaligned due to vibration and shock during vehicle operation
Solution Approach 1:
The system transitions from static initial alignment to dynamic real-time alignment by continuously monitoring IMU data and adjusting lidar sensor alignment parameters during vehicle operation, allowing the system to adapt to changing vibration and shock conditions
Solution Approach 2:
The system uses IMU acceleration data as feedback to detect lidar sensor misalignment caused by vehicle vibrations and shocks, then applies corrective alignment transformations to maintain accurate sensor fusion
2Manufacturing precision
If external alignment targets or procedures are used to realign lidar sensor, then alignment accuracy can be restored, but vehicle operation is interrupted
Solution Approach 1:
The system performs self-alignment by using its own IMU sensor data to detect misalignment and automatically compute correction parameters, eliminating the need for external alignment targets or manual intervention during vehicle operation
Solution Approach 2:
The alignment process continues uninterrupted during vehicle operation by using real-time IMU data streams, allowing the lidar sensor to remain aligned without stopping the vehicle or interrupting normal operation
3Measurement precision
If lidar sensor data is combined with other sensor modalities, then sensor fusion accuracy is improved, but misalignment between sensors degrades the quality of combined data
Solution Approach 1:
The system uses IMU data as an intermediary to detect and correct lidar sensor misalignment, enabling accurate sensor fusion by mediating the alignment between lidar and other sensors through real-time transformation parameters
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 approach enhances sensor fusion by maintaining accurate lidar sensor alignment during vehicle operation, improving the quality and accuracy of point cloud data, which enables precise vehicle path determination and control, without the need for external alignment targets or procedures that interrupt vehicle operation.
Implementation Method 1
Acquiring velocity lidar point cloud data can be based on a doppler shift
Data Source
AI summary
A computer, including a processor and a memory, the memory including instructions to be executed by the processor to obtain velocity lidar point cloud data acquired with a frequency modulated continuous wave (FMCW) lidar sensor, wherein the velocity lidar point cloud data includes a speed with which a data point is moving with respect to the FMCW lidar sensor, filter the velocity lidar point cloud data to select static velocity data points, wherein the static velocity data points are velocity data points each correspond to a point on a roadway around a vehicle. The instructions can include further instructions to determine FMCW lidar sensor accelerations in six degrees of freedom based on the static velocity lidar data points and determine FMCW lidar sensor rotations and translations in six degrees of freedom based on the FMCW lidar sensor accelerations in six degrees of freedom. The instructions can include further instructions to determine vehicle rotations and translations in six degrees of freedom based on inertial measurement unit (IMU) data, determine FMCW lidar sensor mis-alignment based on comparing the FMCW lidar sensor rotations and translations with the vehicle rotations and translations and align the FMCW lidar sensor based on the FMCW lidar sensor mis-alignment. The instructions can include further instructions to operate a vehicle based on the aligned FMCW lidar sensor.