Low-Light Camera Capture Control for Autonomous Navigation
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Solution Overview
Problem
Existing autonomous machines struggle with efficient navigation in low-light conditions, often requiring illumination and long exposure times that compromise battery life and operational speed.
Innovation Solution
Implement a lowlight navigation system that uses camera capture configuration techniques, including photometric calibration and image evaluation, to reduce exposure time and active illumination, enabling improved navigation by trading off mowing speed for increased battery life and manufacturability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If strong illumination and long exposure times are used to facilitate continuous operation in lowlight conditions, then navigation capability is improved, but battery life deteriorates
Solution Approach 1:
The system dynamically adjusts camera capture parameters (exposure time, illumination intensity) based on lighting conditions and machine motion state. In lowlight conditions, the system selectively increases exposure time and illumination only when the machine is stationary or moving slowly, rather than continuously, thereby balancing navigation reliability with energy conservation
Solution Approach 2:
The system changes camera capture parameters (exposure time, gain, illumination intensity) based on detected lighting conditions. In lowlight environments, parameters are adjusted to optimize image quality while considering energy consumption, allowing the system to maintain navigation capability without continuously consuming maximum energy
2Measurement precision
If long exposure times are used for lowlight navigation, then image quality is improved, but operational speed deteriorates
Solution Approach 1:
The system dynamically coordinates camera exposure timing with machine motion state. Long exposure times are applied only when the machine is stationary or moving slowly, while during faster movement or normal lighting conditions, shorter exposure times are used to maintain operational speed. This dynamic coordination allows the system to achieve high image quality when needed without permanently sacrificing operational speed
Solution Approach 2:
The system uses periodic localization updates rather than continuous long-exposure imaging. The camera captures images at intervals when the machine is stationary or slow-moving, allowing long exposure times to be used periodically for high-quality images while maintaining overall operational speed through brief pauses rather than continuous slow operation
3Reliability
If active illumination is used in lowlight conditions, then navigation accuracy is improved, but energy consumption increases
Solution Approach 1:
The system applies illumination locally and selectively - using active illumination only in the specific lowlight conditions where it is needed for navigation accuracy, rather than continuously or in all environments. The illumination is targeted at the work region during localization updates, providing navigation accuracy where required while minimizing overall energy consumption
Solution Approach 2:
The system changes illumination intensity parameters based on detected lighting conditions. In lowlight environments, illumination intensity is increased to improve navigation accuracy, while in normal lighting conditions, illumination is reduced or turned off to conserve energy. This parameter adjustment allows the system to optimize the balance between navigation accuracy and energy consumption
Data Source
AI summary
Autonomous machine navigation techniques include using simulation to configure camera capture parameters. A method may include capturing image data of a scene, generating irradiance image data, determining at least one test camera capture parameter, determining a simulated scene parameter, and generating at least one updated camera capture parameter. Image data for camera capture configuration may be captured while the autonomous machine is moving. Camera captures parameters may be used to capture images while the autonomous machine is slowed or stopped, particularly in lowlight conditions.