Sensor Data Alignment for Autonomous Map Creation and Localization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems for autonomous vehicles and machines struggle to generate accurate maps and perform localization using sensor data from RADAR and LIDAR sensors, particularly in dynamic environments, due to challenges in data processing, alignment, and compression.
Innovation Solution
The system processes RADAR data by aggregating scan data sets, filtering dynamic objects, and compressing data into tiles with delta sets, enabling efficient communication and map generation, while also performing localization by determining pose parameters through alignment and registration of sensor data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data from RADAR and LIDAR is processed to generate accurate maps and perform localization in dynamic environments, then measurement precision and reliability are improved, but device complexity and processing time increase
Solution Approach 1:
The patent segments sensor data processing into distinct functional modules: data acquisition from multiple sensors, dynamic object filtering to remove moving elements, map generation from filtered data, and localization by comparing current sensor data with generated maps. This modular segmentation reduces processing complexity by handling each aspect separately rather than processing all data uniformly.
Solution Approach 2:
The system performs preliminary actions by pre-generating maps from sensor data before localization is needed. These pre-generated maps serve as reference data that can be quickly compared against current sensor readings, significantly reducing the computational burden during actual localization operations. The dynamic object filtering is also performed in advance to clean the data before map generation.
2Productivity
If dynamic objects are filtered and data is compressed into tiles with delta sets for efficient communication, then loss of information increases, but productivity and ease of operation improve
Solution Approach 1:
The patent applies local quality by dividing the sensor data into spatial tiles and applying different processing strategies to different regions. Static regions are compressed more aggressively using delta sets, while regions containing dynamic objects retain higher fidelity. This allows efficient compression overall while preserving critical information where needed.
Solution Approach 2:
The system changes data representation parameters by converting raw sensor data into tiled formats with delta sets. This parameter transformation enables more efficient storage and transmission. The patent also dynamically adjusts filtering parameters based on object classification, applying different levels of data retention to different spatial regions based on their importance.
Data Source
AI summary
One or more embodiments of the present disclosure relate to aligning sensor data. In some embodiments, the aligning may be used for performing localization. In these or other embodiments, the aligning may be used for map creation.