Multi-Resolution Voxel Alignment for Faster Vehicle Mapping
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Solution Overview
Problem
Existing systems face challenges in efficiently aligning multi-resolution voxel spaces, which are crucial for accurate mapping and localization of vehicles in dynamic environments, often requiring significant processing resources and time.
Innovation Solution
The system generates alignments between multi-resolution voxel spaces by initially aligning coarser resolutions, then iteratively adding finer resolutions, while utilizing eigenvalue weights and quality metrics to optimize the alignment process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multi-resolution voxel spaces are aligned using traditional methods, then alignment accuracy is improved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent segments the alignment process by resolution levels, dividing the multi-resolution voxel space alignment into multiple stages. Each stage processes a specific resolution level, allowing the system to handle coarse resolutions first for rapid initial alignment, then progressively refine with finer resolutions. This segmentation enables parallel processing of different resolution layers and reduces the computational burden on any single resolution level, thereby decreasing overall processing time while maintaining alignment accuracy.
Solution Approach 2:
The patent applies preliminary action by performing alignment operations on coarser resolution levels before processing finer resolutions. The coarse-resolution alignment establishes an initial transformation that serves as a foundation for subsequent fine-resolution alignment. This preliminary coarse alignment reduces the search space and computational complexity for finer resolutions, enabling the system to achieve accurate alignment more efficiently by avoiding direct processing of all fine details from the outset.
2Measurement precision
If multi-resolution voxel spaces are aligned using traditional methods, then alignment accuracy is improved, but computational resources required increase significantly
Solution Approach 1:
The patent segments the computational workload by organizing voxel processing into resolution-based groups that can be handled independently. Each resolution level is processed as a separate computational task, allowing for optimized resource allocation. The system can allocate computational resources dynamically across different resolution levels, processing coarse resolutions with fewer resources and reserving higher computational power for fine-resolution details only when needed, thus reducing overall computational resource consumption.
Solution Approach 2:
The patent performs preliminary coarse-resolution alignment to establish an initial transformation framework before engaging computationally intensive fine-resolution processing. This preliminary action reduces the complexity of subsequent computations by providing a pre-established reference frame, thereby minimizing the computational resources required for achieving final alignment accuracy. The coarse alignment serves as a computational shortcut that avoids exhaustive processing of all fine details.
3Productivity
If coarse resolutions are aligned first followed by finer resolutions, then processing efficiency is improved, but system complexity increases
Solution Approach 1:
The patent manages system complexity by segmenting the multi-resolution alignment into modular resolution-level processors. Each module handles a specific resolution level and can be independently configured and processed. This modular segmentation allows the system to scale complexity selectively - only activating the complexity required for the current resolution level being processed - while maintaining a structured framework that simplifies overall system management despite the multi-resolution nature of the task.
Data Source
AI summary
Techniques for representing a scene or map based on statistical data of captured environmental data are discussed herein. In some cases, the data (such as covariance data, mean data, or the like) may be stored as a multi-resolution voxel space that includes a plurality of semantic layers. In some instances, individual semantic layers may include multiple voxel grids having differing resolutions. Multiple multi-resolution voxel spaces may be merged or aligned to generate combined scenes based on detected voxel covariances at one or more resolutions.


