SLAM Hessian Matrix Block Computation for Reduced Power
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Solution Overview
Problem
Simultaneous Localization and Mapping (SLAM) technologies face challenges in performing back-end optimization operations efficiently, particularly in terms of speed and power consumption, due to the large volume of operations required for mapping and location optimization, especially with varying map sizes and sensor data precision.
Innovation Solution
The proposed solution involves an apparatus and method that selectively computes and accumulates elements of an optimization matrix without generating the entire Hessian matrix, using a pipeline structure to perform optimization operations sequentially, and employing Schur-complement operations to reduce the computational burden, allowing for high-speed and low-power processing of SLAM tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire Hessian matrix is generated for all measurements to perform back-end optimization, then the optimization accuracy is improved, but the computational complexity and processing time increase significantly
Solution Approach 1:
The patent segments the Hessian matrix into multiple blocks corresponding to different measurements. Instead of generating and processing the entire Hessian matrix at once, the system divides it into manageable blocks (e.g., H1, H2, H3 for different measurements) that can be processed sequentially or in parallel, reducing the computational burden while maintaining optimization accuracy.
Solution Approach 2:
The patent applies partial action by computing only the necessary blocks of the Hessian matrix required for the current optimization iteration rather than the entire matrix. The system selectively generates Hessian blocks for specific measurements that contribute most to the optimization, avoiding unnecessary computations while preserving the essential optimization functionality.
2Reliability
If the entire Hessian matrix is generated for all measurements, then the optimization completeness is improved, but the memory requirements increase significantly
Solution Approach 1:
The patent segments the Hessian matrix into multiple blocks that can be stored and processed separately in memory. Each measurement contributes a specific block (e.g., H1 for measurement 1, H2 for measurement 2), allowing the system to manage memory usage efficiently by loading and processing blocks in a structured manner rather than requiring the entire matrix to reside in memory simultaneously.
Solution Approach 2:
The patent computes and stores only the necessary Hessian blocks required for the optimization process rather than the complete Hessian matrix. This partial computation approach reduces the total memory footprint while ensuring that all essential information for optimization completeness is retained through selective block generation and accumulation.
3Measurement precision
If a large volume of operations is performed for back-end optimization with varying map sizes and sensor data, then the SLAM accuracy is improved, but the power consumption increases
Solution Approach 1:
The patent segments the optimization operations into discrete blocks corresponding to individual measurements and Hessian blocks. This segmentation allows the system to process operations in smaller, more efficient units that can be executed with lower power consumption per unit time, while still achieving the required SLAM accuracy through cumulative processing of all necessary blocks.
Solution Approach 2:
The patent performs partial computation of Hessian matrix elements by generating only the specific blocks needed for the current optimization state rather than computing the entire matrix. This reduces the total number of operations required, directly lowering power consumption while maintaining SLAM accuracy through selective processing of critical measurement data.
4Reliability
If the entire Hessian matrix is generated for all measurements, then the optimization thoroughness is improved, but the processing speed decreases
Solution Approach 1:
The patent segments the Hessian matrix generation and optimization process into parallelizable blocks corresponding to different measurements. Each block (H1, H2, H3, etc.) can be computed and accumulated independently, enabling parallel processing across multiple cores or processing units. This segmentation maintains optimization thoroughness while significantly improving processing speed through concurrent execution of block computations.
Solution Approach 2:
The patent computes only the essential Hessian blocks required for the current optimization iteration rather than generating the complete Hessian matrix beforehand. This partial computation approach reduces the total processing time while maintaining optimization thoroughness by focusing computational resources on the most relevant measurement blocks for the current state.
Data Source
AI summary
Provided is a processor configured to compute elements affecting an optimization matrix in connection with a first measurement, among elements of a Hessian matrix, instead of generating a whole Hessian matrix for a map point and a camera pose based on all measurements, and accumulate the computed elements over the optimization matrix used to perform optimization operations in relation to states of the map point and the camera pose.