Visual Landmark Mapping for Reliable Robot Navigation
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Solution Overview
Problem
Existing vision-based guidance systems for robotic devices face issues with navigation due to unreliable visual features, which can lead to errors in pose estimation and potential crashes, as these features may change appearance, location, or be obstructed, making it difficult for robots to consistently and accurately navigate uncontrolled environments.
Innovation Solution
A system that generates an environment map using a subset of images collected by a drone or robotic device, identifies and classifies visual landmarks, determines their reliability scores based on stability and appearance consistency, and filters out unreliable features and landmarks, allowing the robot to navigate using only stable visual features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system uses all visual features in the environment for navigation mapping, then the map coverage is comprehensive, but the reliability of navigation deteriorates due to unreliable visual features causing pose estimation errors and potential crashes
Solution Approach 1:
The system segments visual features into reliable and unreliable categories by analyzing temporal consistency across multiple images. Features that maintain consistent appearance and position over time are identified as reliable landmarks, while those that change are filtered out. This segmentation allows the system to build a navigation map using only reliable features, resolving the contradiction between map completeness and navigation reliability.
Solution Approach 2:
The system changes the parameter of feature selection from static (using all features) to dynamic (using only temporally consistent features). By introducing temporal consistency as a selection criterion and adjusting the reliability threshold parameter, the system adapts to different environmental conditions while maintaining reliable navigation, thus resolving the contradiction between comprehensive coverage and reliable navigation.
2Measurement precision
If the system filters out unreliable visual features to improve navigation accuracy, then pose estimation accuracy improves, but the processing time increases due to additional image analysis and feature validation
Solution Approach 1:
The system performs preliminary analysis of visual features during the mapping phase, establishing temporal consistency profiles for all features before navigation begins. This preliminary action pre-identifies reliable landmarks and builds a filtered feature database, so that during actual navigation, the system only needs to query pre-validated features rather than analyzing all features in real-time, thus improving pose estimation accuracy without excessive processing time during critical navigation operations.
Solution Approach 2:
The system implements periodic re-evaluation of feature reliability at scheduled intervals during navigation, rather than continuous analysis. This periodic action maintains pose estimation accuracy by updating the reliable feature set when necessary, while minimizing processing time by avoiding constant re-analysis of all visual features, thus resolving the contradiction between measurement precision and time loss.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for navigation using planar landmarks. In some implementations, images depicting an area of a property captured by a camera are obtained. Landmarks in the area are identified based on the images. An expected stability for each of the landmarks are determined. A map for the area is generated based on the expected stability for each of the landmarks. The map is transmitted to one or more electronic devices.