Adaptive Lighting Navigation for Stable Robotic Pose Estimation
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Solution Overview
Problem
Vision-based guidance systems for robotic devices face challenges in navigating environments with varying lighting conditions, as changes in lighting can confuse localization algorithms and degrade image quality, leading to errors in pose estimation and potential loss of device control.
Innovation Solution
A system that maps lighting sources and their effects on visual features within an environment, allowing for dynamic adjustments in camera settings and lighting conditions to optimize navigation, pose estimation, and image quality by leveraging sensor data and onboard systems like cameras and ambient light sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If standard auto-exposure techniques are used to maintain acceptable image quality in changing light, then image quality is improved, but visual features used by odometry and localization algorithms change or disappear, resulting in errors in pose estimation
Solution Approach 1:
The system performs preliminary mapping of lighting sources and their effects on visual features before navigation. By pre-characterizing how different light sources affect feature appearance, the system can predict and prepare for lighting changes, adjusting camera settings in advance to maintain both image quality and feature trackability during navigation.
Solution Approach 2:
The system dynamically adjusts camera exposure parameters based on predicted lighting conditions. By using the pre-built lighting map, the system can proactively modify exposure settings to compensate for upcoming lighting changes, ensuring that visual features remain detectable and trackable while maintaining acceptable image quality throughout navigation.
2Illumination intensity
If rapid changes to auto-exposure settings are made to react to changing light, then image quality is maintained, but visual features change or disappear, causing errors in pose estimation or loss of device control
Solution Approach 1:
The system performs preliminary mapping of lighting sources and their effects on visual features before navigation. By pre-characterizing how different light sources affect feature appearance, the system can predict and prepare for lighting changes, adjusting camera settings in advance to maintain both image quality and feature trackability during navigation.
Solution Approach 2:
The system uses feedback from the lighting map and real-time sensor data to continuously adjust camera settings. By monitoring actual lighting conditions against the pre-built model, the system can make smooth, informed adjustments to exposure settings, avoiding rapid changes that would cause feature disappearance while maintaining image quality and navigation reliability.
3Adaptability or versatility
If vision-based guidance systems attempt to create a map based on visual features, then navigation capability is improved, but the features are highly dependent on lighting, angle of lighting, surface texture and specularity
Solution Approach 1:
The system performs preliminary mapping of lighting sources and their effects on visual features before navigation. By pre-characterizing how different light sources affect feature appearance, the system can predict and prepare for lighting changes, adjusting camera settings in advance to maintain both image quality and feature trackability during navigation.
Solution Approach 2:
The system introduces an intermediary lighting map that models the relationship between light sources and visual features. This intermediary representation allows the system to decouple navigation from direct dependence on actual lighting conditions by using the lighting model to predict and compensate for lighting effects, thereby reducing the harmful impact of lighting variability on navigation.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for lighting adaptive navigation. In some implementations, a method includes receiving map data associated with a property; obtaining sensor data; based on the map data and the sensor data, determining a lighting scenario; and based on the lighting scenario, configuring the robotic device.