Camera-Based Wheel Slip Detection for Autonomous Navigation
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating due to the vast amount of data they need to process, analyze, and store, including image data, map data, and sensor data, which can limit their navigation capabilities.
Innovation Solution
The system uses cameras to provide autonomous vehicle navigation features by analyzing images to determine motion indicators, predicting wheel rotation, detecting wheel slip conditions, and initiating navigational actions accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional mapping technology is used for navigation, then the vehicle can navigate using existing infrastructure, but the sheer volume of data needed to store and update the map poses daunting challenges
Solution Approach 1:
The system segments the navigation data into two parts: a compact sparse map containing only essential geometric information (road boundaries, intersections, connectivity) and detailed visual information captured in real-time by onboard cameras. This segmentation reduces the data storage burden while maintaining navigation reliability.
Solution Approach 2:
The patent introduces visual inertial odometry (VIO) as an intermediary system that bridges the gap between the compact sparse map and the vehicle's actual position. The VIO system processes real-time camera images and IMU data to estimate vehicle pose, reducing dependence on large-scale detailed mapping while maintaining accurate navigation.
2Measurement precision
If vast volumes of data are collected and analyzed for autonomous navigation, then navigation accuracy can be improved, but the challenges can in fact limit or even adversely affect autonomous navigation
Solution Approach 1:
The system extracts only the essential geometric features from the environment to build a sparse map, leaving out detailed visual information. This extraction process reduces data processing complexity while maintaining the ability to achieve accurate navigation through visual inertial odometry using real-time camera feeds.
Solution Approach 2:
The system performs preliminary action by pre-processing and storing only critical geometric information in the sparse map during vehicle operations. This preliminary data preparation reduces the computational burden during real-time navigation while preserving navigation accuracy.
3Reliability
If image analysis is used to determine motion and detect wheel slip, then navigation reliability can be improved, but processing speed may be reduced
Solution Approach 1:
The system merges image analysis with inertial measurement unit (IMU) data to detect wheel slip conditions. By combining visual information from cameras with motion data from accelerometers and gyroscopes, the system achieves reliable wheel slip detection while maintaining real-time processing speed through sensor fusion.
Solution Approach 2:
The system performs partial action by analyzing only specific regions of interest in the captured images (such as road surface features near the vehicle) rather than processing entire image frames. This selective analysis maintains navigation reliability while improving processing speed for real-time wheel slip detection.
Data Source
AI summary
The present disclosure relates to systems and methods for identifying a wheel slip condition. In one implementation, a processor may receive a plurality of image frames acquired by an image capture device of a vehicle. The processor may also determine based on analysis of the images one or more indicators of a motion of the vehicle; and determine a predicted wheel rotation corresponding to the motion of the vehicle. The processor may further receive sensor outputs indicative of measured wheel rotation associated with a wheel; and compare the predicted wheel rotation to the measured wheel rotation for the wheel. The processor may additionally detect a wheel slip condition wheel based on a discrepancy between the predicted wheel rotation and the measured wheel rotation; and initiate at least one navigational action in response to the detected wheel slip condition associated with the wheel.