LiDAR Point Cloud Reformatting for Spatial Information
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Solution Overview
Problem
Autonomous vehicles face inefficiencies in processing raw sensor data, such as LiDAR point cloud data, due to the lack of spatial information, which hinders tasks like object detection and tracking, requiring excessive processing capacity and time.
Innovation Solution
Converting sensor data into grid-based sensor data by determining a grid structure and associating it with the data, adding spatial information and enabling efficient downstream processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If raw sensor data is processed directly, then processing capacity and time are excessive, but spatial information is insufficient for effective object detection and tracking
Solution Approach 1:
The patent segments the sensor data processing by dividing the point cloud into multiple depth ranges (e.g., near, mid, far zones). Each depth range is processed independently through separate neural network branches, allowing spatial information to be preserved and enhanced at different scales while distributing computational load efficiently across multiple processing paths rather than overwhelming a single processor.
Solution Approach 2:
The patent transforms the raw point cloud data by adding depth dimension information through multi-scale processing. By processing data at multiple depth ranges and combining features from different scales, the system enriches the spatial representation from 3D coordinates to multi-dimensional spatial features, enabling better object detection while maintaining processing efficiency through hierarchical feature extraction.
2Measurement precision
If complex calculations are performed on raw sensor data for autonomous vehicle operations, then object detection and tracking accuracy improves, but processing time and computational resources increase significantly
Solution Approach 1:
The patent divides the complex object detection task into multiple simpler sub-tasks processed in parallel, with each neural network branch handling a specific depth range. This segmentation allows the system to achieve high detection accuracy through specialized processing for each zone while reducing overall processing time by distributing computations across multiple independent paths that can execute simultaneously.
Solution Approach 2:
The patent performs preliminary processing by organizing point cloud data into depth-based groups before main detection. This pre-organization of spatial information allows subsequent detection algorithms to work with pre-filtered, structured data rather than raw unprocessed points, significantly reducing the computational complexity and time required for accurate object detection and tracking.
3Reliability
If all sensor data is processed for downstream tasks, then comprehensive object detection is achieved, but processing complexity and computational load increase
Solution Approach 1:
The patent segments the processing pipeline into multiple depth-based branches, each handling a specific spatial zone. This segmentation maintains detection reliability by ensuring comprehensive coverage of all spatial regions while reducing processing complexity through localized processing - each branch only processes relevant data for its depth range rather than all data globally, simplifying the overall computational structure.
Solution Approach 2:
The patent applies local quality processing by using different processing strategies for different depth zones. Each depth range receives tailored processing appropriate to its characteristics (e.g., near objects require different attention than far objects), improving detection reliability for each local region while reducing overall system complexity by avoiding a one-size-fits-all approach that would require processing all data uniformly through complex algorithms.
Data Source
AI summary
Described herein are systems, methods, and computer readable media for performing data conversion on sensor data to obtain modified sensor data that is formatted/structured appropriately for downstream processes that rely on the sensor data as input. The sensor data can include point cloud data captured by a LiDAR, for example. A grid structure and corresponding grid characteristics can be determined and the sensor data can be converted to grid-based sensor data by associating the grid structure and its characteristics with the sensor data. Generating the grid-based sensor data can include reformatting the point cloud data to superimpose the grid structure and its grid characteristics onto the point cloud data. Various downstream processing that cannot feasibly be performed on the raw sensor data can then be performed efficiently on the modified grid-based sensor data by virtue of the grid structure imbuing the sensor data with spatial proximity information.