Hypothesis-Based Spatial Parsing for Low-Compute Localization
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Solution Overview
Problem
Existing SLAM algorithms are computationally expensive, prone to convergence failures due to sensor noise and environmental changes, and suffer from high memory consumption, limiting their deployment on resource-constrained platforms.
Innovation Solution
A method and system that utilize a series of observations and displacements by an agent in a space to determine attributes, such as objects or destinations, by comparing current observations with stored data and adjusting hypotheses based on similarity measures, reducing computational and memory intensity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If feature extraction and bundle adjustment are used in SLAM algorithms, then localization and mapping accuracy is improved, but computational cost and processing time increase significantly
Solution Approach 1:
The patent segments the SLAM problem into distinct modules: feature extraction front-end, hypothesis generation middle-end, and verification back-end. Each module operates semi-independently with defined interfaces, allowing parallel processing of multiple hypotheses while maintaining localization accuracy through systematic exploration of possible feature correspondences
Solution Approach 2:
The system performs preliminary actions by generating multiple candidate hypotheses about feature correspondences and camera poses before verification. This advance preparation allows the back-end to efficiently evaluate pre-computed candidates rather than solving the full optimization problem from scratch, reducing real-time computational burden while maintaining accuracy
2Measurement precision
If bundle adjustment is used to track camera location and feature locations, then mapping accuracy is improved, but the system becomes vulnerable to convergence failure due to sensor noise and environmental changes
Solution Approach 1:
The patent implements beforehand cushioning by preparing multiple candidate hypotheses in advance that can serve as fallback options if the primary bundle adjustment convergence fails. The system maintains a pool of pre-computed feature correspondence hypotheses that can be activated when sensor noise or environmental changes cause the primary tracking to diverge, ensuring continuous operation
Solution Approach 2:
The system employs feedback mechanisms where the back-end verification results are fed back to the middle-end hypothesis generation, which adjusts its strategy based on convergence success or failure. When bundle adjustment fails to converge, the feedback triggers regeneration of alternative hypotheses, creating a closed-loop system that adapts to maintain both accuracy and reliability
3Reliability
If outlier rejection is implemented to reduce bundle adjuster convergence failure, then reliability is improved, but computational cost increases significantly
Solution Approach 1:
The patent segments the outlier rejection function into the back-end verification module, separate from the front-end feature extraction. This segmentation allows outlier rejection to operate on pre-processed feature candidates using efficient geometric constraints and statistical tests, reducing the computational burden compared to traditional approaches that perform outlier rejection continuously during feature tracking
4Reliability
If Visual-Inertial Odometry is used to compensate for convergence failure, then system robustness is improved, but position error accumulates over time
Solution Approach 1:
The patent merges Visual-Inertial Odometry (VIO) with the hypothesis-based SLAM system by integrating VIO-provided camera pose estimates as additional hypotheses in the middle-end. The back-end then verifies these VIO-generated hypotheses using the same rigorous geometric constraint checks, allowing the system to benefit from VIO's robustness to convergence failure while maintaining position accuracy through verification against ground truth constraints
5Loss of information
If feature extraction processes all image data to locate distinct regions, then feature detection completeness is improved, but memory consumption increases
Solution Approach 1:
The patent extracts only the essential elements needed for SLAM: distinctive feature points and their geometric relationships. The front-end extracts feature descriptors and the middle-end extracts hypothesized correspondences, storing only the minimal necessary data (feature locations, descriptors, and correspondence hypotheses) rather than retaining entire images or exhaustive feature sets, thereby reducing memory consumption while maintaining detection completeness
Data Source
AI summary
A computer implemented method and system are provided for parsing a multi-dimensional space based on a series of observations and displacements performed by an agent in the space, to find an attribute of the space. The method includes making sequential observations and displacements from locations of the agent in the space, and comparing the observations and displacements to a set of stored observations and displacements to identify a hypothesis of the attribute of the space and to test the hypothesis, by obtaining an observation comparison measure, and/or a displacement comparison measure, and adjusting, maintaining or confirming the hypothesis based on the observation comparison measure and/or the displacement comparison measure.