Autonomous Navigation Camera Tuning for Low-Light Localization
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Solution Overview
Problem
Autonomous machines face challenges in navigating low-light environments, such as night, dawn, or dusk, due to limitations in existing image capturing and navigation systems.
Innovation Solution
The implementation of a lowlight navigation system that includes automatic camera capture configuration, allowing for reduced exposure times and minimal use of active illumination, enabling improved navigation at night by trading off mowing speed for increased battery life and manufacturability.
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 energy consumption increases
Solution Approach 1:
The system dynamically adjusts camera exposure time and illumination intensity based on ambient light conditions. In lowlight environments, the camera increases exposure time to capture sufficient light, while reducing or eliminating the need for active illumination sources, thereby maintaining navigation capability while conserving energy.
Solution Approach 2:
The navigation system transitions between different operational modes (continuous operation vs. slow and stare) based on lighting conditions. In lowlight conditions, it adopts a dynamic approach where the machine slows down or stops momentarily to capture images with longer exposure times, optimizing the balance between navigation reliability and energy consumption.
2Measurement precision
If long exposure times are used for image capture in lowlight conditions, then navigation accuracy is improved, but movement speed decreases
Solution Approach 1:
The system implements periodic localization events where the machine intermittently slows down or stops to capture high-quality images with longer exposure times. Between these periodic localization events, the machine operates at normal speed, thus achieving accurate navigation without continuous speed reduction.
Solution Approach 2:
The machine skips continuous slow operation by using brief momentary stops or slow-downs only when localization is needed. It rushes through intermediate segments at normal speed, capturing essential navigation data efficiently without sacrificing overall progress.
3Measurement precision
If illumination is used during image recording in lowlight conditions, then image quality is improved, but battery life decreases
Solution Approach 1:
The system adjusts camera parameters (exposure time, gain) to maximize light capture from ambient sources. By optimizing these parameters, the system achieves adequate image quality without activating power-consuming illumination sources, thereby preserving battery life during lowlight operation.
4Productivity
If camera exposure time is reduced for continuous operation, then productivity is improved, but navigation accuracy in lowlight conditions deteriorates
Solution Approach 1:
The system maintains high productivity through continuous operation at normal speed, but periodically interrupts this flow to perform localization with longer exposure times when lighting conditions warrant it. This periodic approach ensures navigation accuracy is maintained without significantly compromising overall productivity.
Data Source
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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.