Multiresolution Voxel Space for Memory-Efficient Sensor Mapping
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Solution Overview
Problem
In resource-constrained environments, increasing the size or density of voxel spaces for sensor data representation leads to significant memory and processing challenges due to the large amount of data involved, particularly in capturing and processing sensor data such as lidar, radar, or time-of-flight data.
Innovation Solution
A multiresolution voxel space is introduced, which consists of multiple levels with varying resolutions, allowing data to be represented at higher resolution near the sensor and lower resolution further away, with intelligent selection of levels for processing based on data availability, and using techniques like ray casting and segmentation to efficiently manage data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the size or density of voxel space is increased to improve sensor data representation, then measurement precision is improved, but memory usage and processing requirements increase significantly
Solution Approach 1:
The voxel space is divided into multiple levels with different resolutions. Each level represents a different scale of detail, allowing the system to segment the environment into coarse and fine regions. This segmentation enables efficient memory management by storing only necessary detail at each level rather than uniformly high resolution throughout the entire space.
Solution Approach 2:
Different regions of the voxel space are assigned different resolution levels based on their importance. Regions closer to the sensor or containing dynamic objects use higher resolution levels for better measurement precision, while distant or static regions use lower resolution levels to reduce memory consumption. This local quality approach optimizes the balance between precision and resource usage.
2Measurement precision
If the size or density of voxel space is increased to improve sensor data representation, then measurement precision is improved, but processing requirements increase significantly
Solution Approach 1:
Processing operations are performed at appropriate resolution levels based on the task requirements. By segmenting processing into multiple levels of detail, the system avoids performing computationally intensive operations at the highest resolution everywhere, thereby improving processing efficiency while maintaining necessary precision for critical tasks.
Solution Approach 2:
The system performs processing at the minimum necessary resolution level for each task rather than always using maximum resolution. This partial action approach applies processing only where and at the level needed, reducing overall computational burden while maintaining adequate measurement precision for sensor data representation and object identification.
3Measurement precision
If uniform high resolution is used throughout the voxel space, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The voxel space structure is made dynamic with multiple adjustable resolution levels rather than a fixed uniform grid. This dynamic multiresolution structure allows the system to adapt resolution to task requirements, reducing management complexity by only maintaining high resolution where needed while using lower resolution elsewhere, thereby decreasing overall device complexity.
Solution Approach 2:
The multiresolution voxel space serves multiple functions simultaneously: it provides high precision measurement where needed, reduces memory usage in less critical areas, and enables efficient processing at appropriate levels. This universal structure handles diverse requirements (memory efficiency, processing speed, precision) within a single unified framework, reducing the need for separate systems and lowering overall device complexity.
Data Source
AI summary
A multiresolution voxel space is discussed herein. Data can be represented in individual levels in the multiresolution voxel space. A first level can correspond to a first region of an environment and a second level can correspond to a second region of an environment, the second region corresponding to a subset of the first region. In some examples, the levels can comprise a same number of voxels, such that the first level covers a large, low resolution region, while the second level covers a smaller, higher resolution region, though more levels are contemplated. Data represented in the voxel spaces can be processed at higher resolution where available and at a lower resolution where a data density is lower and combined in an intelligent manner. Voxel spaces can be updated based on movement of the sensor providing the data.