Lowlight Camera Exposure Tuning for Autonomous Machine Navigation
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Solution Overview
Problem
Autonomous machines face challenges in navigating lowlight conditions, such as night, dawn, or dusk, due to the need for illumination which can slow down movement and reduce battery life, and existing techniques often compromise mowing speed for improved navigation.
Innovation Solution
The implementation of a lowlight navigation system that uses camera capture configurations, including photometric calibration and image evaluation, to reduce exposure time and active illumination, allowing for improved navigation by capturing well-exposed images for localization, and includes techniques like 'slow and stare' to balance speed and battery life.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If strong illumination source is used to facilitate continuous operation in lowlight conditions, then navigation capability is improved, but battery life is reduced
Solution Approach 1:
The system uses periodic illumination bursts synchronized with the camera shutter rather than continuous illumination. The illumination source activates only during brief exposure windows when the camera captures images, creating a pulsed illumination pattern that provides necessary light while dramatically reducing overall energy consumption during lowlight navigation operations.
Solution Approach 2:
The system pre-calculates and stores irradiance maps and camera capture parameters before actual navigation. By preparing illumination strategies and exposure settings in advance based on predicted lowlight conditions, the system avoids real-time computation overhead and can execute pre-planned energy-efficient illumination sequences without compromising navigation reliability.
2Measurement precision
If long exposure time is used to capture images in lowlight conditions, then navigation accuracy is improved, but movement speed is reduced
Solution Approach 1:
The system dynamically adjusts camera exposure time based on real-time irradiance measurements and navigation requirements. Rather than using fixed long exposure times, the exposure duration is adaptively optimized to capture sufficient image detail for accurate localization while minimizing the time the machine must slow down or stop, thereby maintaining higher movement speeds.
Solution Approach 2:
The system changes multiple camera capture parameters simultaneously including exposure time, gain, and illumination intensity to achieve acceptable image quality in lowlight conditions. By adjusting these parameters in combination rather than relying solely on long exposure, the system reduces the duration of speed reduction needed while maintaining navigation accuracy.
3Measurement precision
If active illumination is increased to improve localization, then image quality is improved, but energy consumption is increased
Solution Approach 1:
The system uses feedback from irradiance sensor measurements to dynamically adjust illumination intensity and camera exposure parameters. By continuously monitoring the actual light levels in the environment and comparing them against thresholds for acceptable image quality, the system optimizes illumination usage to achieve sufficient localization accuracy while minimizing energy consumption from the illumination source.
Solution Approach 2:
The system applies partial illumination action by using brief illumination bursts only when and where needed for camera exposure, rather than continuous full-intensity illumination. This partial action provides just enough light to achieve acceptable localization accuracy while significantly reducing overall energy consumption compared to sustained high-intensity illumination.
Data Source
AI summary
Autonomous machine (100) navigation techniques include using simulation to configure camera (133) 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 (133) captures parameters may be used to capture images while the autonomous machine (100) is slowed or stopped, particularly in lowlight conditions.