Mobility Vehicle Camera SLAM with Movement Data Fallback
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Solution Overview
Problem
Camera-based indoor environment SLAM systems face challenges in accurately extracting feature points due to disturbances like white walls and strong light, leading to potential termination when feature points are lost for a long period, and are costly when using lidar.
Innovation Solution
A system and method for SLAM using a camera and movement data sensors, such as encoders or inertial sensors, to track feature points and continue localization even when feature points are lost, utilizing movement data to update the vehicle's location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If camera-based SLAM is used instead of lidar, then cost is reduced, but feature point extraction accuracy deteriorates due to disturbances like white walls and strong light
Solution Approach 1:
The patent introduces movement data sensors (encoders, inertial sensors) as intermediary components that mediate between the camera-based SLAM system and the navigation task. When feature points are lost due to disturbances, these sensors provide alternative localization information to maintain system functionality, resolving the contradiction between cost reduction and measurement precision.
2Measurement precision
If feature point tracking is used for SLAM, then localization accuracy is improved, but system reliability deteriorates when feature points are lost for long periods
Solution Approach 1:
The patent changes the operational parameters of the SLAM system by switching between two localization modes: feature point-based mode for high precision when available, and movement data-based mode for reliability when feature points are lost. This parameter switching resolves the contradiction between measurement precision and system reliability.
Solution Approach 2:
The system implements feedback mechanisms to monitor feature point tracking status and automatically switch between localization methods based on real-time conditions. When feature points are lost for exceeding a threshold duration, the system feedbacks to switch to movement data-based localization, ensuring continuous operation.
3Measurement precision
If SLAM terminates when feature points are lost, then measurement precision is maintained, but productivity deteriorates due to navigation interruption
Solution Approach 1:
The patent ensures continuity of useful action by implementing fallback localization using movement data sensors when feature point tracking fails. Instead of terminating SLAM when feature points are lost, the system continues navigation by switching to alternative localization methods, thereby maintaining both precision (when possible) and productivity (when feature points are unavailable).
Data Source
AI summary
A system for Simultaneous Localization and Mapping (SLAM) for a mobility vehicle includes a camera configured to acquire a front image of the mobility vehicle and a movement data sensor configured to detect movement data of the mobility vehicle. The system also includes a controller configured to receive the front image from the camera and detect a feature point from the front image. The controller is also configured to receive the movement data from the movement data sensor, track the detected feature point, and store a state of tracking the feature point. The state is selected from a first state indicating that a previously tracked first feature point is being tracked normally, a second state indicating that the first feature point has been lost and a second feature point is being tracked, and a third state indicating that the first and second feature points have been lost.