Adaptive Motion Compensation for Sensor Scan Accuracy
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Solution Overview
Problem
Existing motion compensation systems for autonomous vehicles fail to accurately account for changes in position and orientation due to time delays between sensor data capture and processing, leading to inaccuracies in representing the environment around the vehicle.
Innovation Solution
An adaptive motion compensation system that generates a motion model based on motion data from sensors like IMUs and wheel encoders, allowing for accurate compensation of sensor data scans to reflect the vehicle's current position and orientation, even during delays in data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sensor data is processed with a time delay to generate scans, then processing time is reduced and productivity is improved, but measurement precision deteriorates due to motion changes during the delay period
Solution Approach 1:
The system performs preliminary motion compensation by generating a motion model based on motion data obtained during the processing delay period. This motion model is then applied to adjust the sensor scan data before it is used for environmental representation, effectively preparing the compensation action in advance to counteract the effects of the processing delay.
Solution Approach 2:
The system continuously obtains motion data from motion sensors and uses this feedback to dynamically update the motion model. This feedback loop allows the system to adapt to changing motion conditions and maintain accurate environmental representation despite processing delays by constantly adjusting the compensation based on actual motion measurements.
2Measurement precision
If motion compensation is applied to sensor scans, then measurement precision is improved, but device complexity increases due to additional motion modeling and compensation computations
Solution Approach 1:
The system introduces a motion model as an intermediary between the raw sensor scan data and the final environmental representation. This motion model acts as a mediator that translates motion data into spatial transformations, simplifying the overall compensation process by breaking it down into manageable steps: obtain motion data, generate motion model, apply transformation to scan data.
3Measurement precision
If motion data is captured at high rates to improve motion model accuracy, then measurement precision is improved, but use of energy increases due to higher sampling frequencies
Solution Approach 1:
The system dynamically adjusts the motion compensation process based on actual motion conditions. The motion model is generated and updated only when motion data indicates significant movement, allowing the system to maintain measurement precision during motion while reducing computational overhead and energy consumption during stationary periods by using a simplified initial motion model.
Data Source
AI summary
A method may include obtaining sensor data describing a total measurable world around a motion sensor. The method may include processing the sensor data to generate a pre-compensation scan of the total measurable world around the motion sensor based on the sensor data. The method may include determining a delay between the obtaining the sensor data and the generation of the pre-compensation scan. The method may include obtaining motion data corresponding to motion of the motion sensor and generating a motion model of the motion sensor based on the motion data. The method may include generating an after-compensation scan of the motion sensor using the delay and the motion model to compensate for continued motion during the delay.