Visual Map Relocalization Using Trajectory-Based Hypothesis Refinement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional relocalization methods face challenges in achieving precise and robust self-relocalization due to environmental changes, local ambiguity in visual maps, and unobserved viewing angles, leading to mismatches and false relocalization results.
Innovation Solution
A system and method for self-relocalization that involves capturing initial visual measurements, establishing relocalization hypotheses, tracking movement trajectories, and refining these hypotheses using additional visual data to reject incorrect poses, thereby improving relocalization accuracy and eliminating ambiguity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional snapshot-based relocalization methods are used, then the system is simple and fast, but relocalization accuracy deteriorates due to environmental changes, local ambiguity, and unobserved viewing angles
Solution Approach 1:
The system transitions from static snapshot-based relocalization to dynamic trajectory-based relocalization. Instead of relying on a single static image, the system processes continuous visual data along the sensor's movement trajectory, enabling the system to adapt to environmental changes and resolve ambiguities through temporal dynamics
Solution Approach 2:
The system adds the temporal dimension to relocalization by incorporating trajectory information. Instead of analyzing only spatial relationships in a single snapshot, the system utilizes the fourth dimension (time) by tracking sensor positions and orientations along the movement trajectory, providing additional constraints for hypothesis verification
2Adaptability or versatility
If multiple relocalization hypotheses are generated to handle ambiguity, then coverage of possible locations improves, but false positives increase without refinement
Solution Approach 1:
The system implements feedback mechanisms where trajectory information and additional visual measurements are used to verify and refine relocalization hypotheses. Hypotheses that are inconsistent with observed trajectory or fail to explain subsequent visual measurements are rejected, creating a closed-loop verification process that reduces false positives
Solution Approach 2:
The system performs preliminary hypothesis generation based on initial visual measurements, then uses subsequent trajectory and visual data to refine and reject incorrect hypotheses. This two-stage approach allows comprehensive initial coverage while ensuring final reliability through verification
3Reliability
If the sensor moves to gather additional visual measurements, then relocalization robustness improves, but time consumption increases
Solution Approach 1:
The system processes visual measurements continuously along the sensor's natural movement trajectory rather than requiring dedicated relocalization maneuvers. By utilizing the continuous stream of visual data already captured during normal operation, the system maintains robustness without significant time penalty
Solution Approach 2:
The system efficiently processes trajectory and visual measurement data through optimized hypothesis refinement algorithms that quickly eliminate incorrect hypotheses. By rapidly filtering out false positives using trajectory constraints, the system achieves robust relocalization with minimal additional processing time
Data Source
AI summary
Described herein are systems and methods that improve the success rate of relocalization and eliminate the ambiguity of false relocalization by exploiting motions of the sensor system. In one or more embodiments, during a relocalization process, a snapshot is taken using one or more visual sensors and a single-shot relocalization in a visual map is implemented to establish candidate hypotheses. In one or more embodiments, the sensors move in the environment, with a movement trajectory tracked, to capture visual representations of the environment in one or more new poses. As the visual sensors move, the relocalization system tracks various estimated localization hypotheses and removes false ones until one winning hypothesis. Once the process is finished, the relocalization system outputs a localization result with respect to the visual map.