Autonomous Mower Vision Localization With Selective Camera Processing
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Solution Overview
Problem
Existing autonomous ground maintenance vehicles, such as lawn mowers, face challenges in efficiently utilizing vision-related computing resources and maintaining flexibility in navigation tasks, particularly when relying on vision-based sensors for boundary confinement and localization.
Innovation Solution
Implement a vision system with multiple cameras, including a localization camera and visual odometry cameras, to determine the machine's pose relative to a predefined work region using a 3D point cloud, reducing computing resources by optimizing image processing and enhancing navigation flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple vision sensors are used for navigation and localization, then measurement precision and reliability are improved, but computing resource utilization and device complexity increase
Solution Approach 1:
The patent divides the vision system into specialized components: boundary detection sensors, localization sensors, and visual odometry sensors. Each sensor type is optimized for its specific function, processing only relevant image data portions. This segmentation reduces the computational burden on each component while maintaining overall system precision through coordinated operation of multiple specialized sensors.
2Measurement precision
If vision-based sensors are used for boundary confinement and localization, then navigation accuracy is improved, but computing resource utilization increases
Solution Approach 1:
The patent applies local quality by processing only specific portions of image data from vision sensors. Boundary detection processes only edge and contour information, while localization processes only distinctive feature points. Visual odometry processes only motion-related changes between frames. This selective processing of local image qualities significantly reduces computing resource requirements while preserving navigation accuracy.
Solution Approach 2:
The system uses partial action by not processing complete high-resolution images for all navigation functions. Instead, it extracts and processes only the necessary portions: boundary edges for confinement, feature points for localization, and motion vectors for odometry. This partial processing approach maintains navigation accuracy while reducing computational energy consumption.
3Measurement precision
If all vision sensors process complete image data, then measurement precision is improved, but productivity and processing speed decrease
Solution Approach 1:
The patent segments the image processing workflow into parallel specialized pipelines: boundary detection pipeline, localization pipeline, and visual odometry pipeline. Each pipeline processes specific features independently, allowing simultaneous execution without full-image processing bottlenecks. This segmentation enables real-time pose determination while maintaining precision through coordinated results from all pipelines.
Solution Approach 2:
The system performs preliminary action by pre-identifying and storing feature points, boundaries, and landmarks in the work area before autonomous operation begins. During navigation, the vision sensors only need to match observed features against these pre-identified references rather than processing complete images for feature extraction. This preliminary preparation significantly speeds up real-time pose determination while maintaining accuracy.
Data Source
AI summary
Vision systems for autonomous machines and methods of using same during machine localization are provided. Exemplary systems and methods may reduce computing resources needed to perform vision-based localization by selecting the most appropriate camera from two or more cameras, and optionally selecting only a portion of the selected camera's field of view, from which to perform vision-based location correction. Other embodiments may provide camera lens coverings that maintain optical clarity while operating within debris-filled environments.