Semantic Grid Mapping for Real-Time Autonomous Navigation
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Solution Overview
Problem
The generation of detailed high-definition maps for autonomous and semi-autonomous vehicles is challenging, and maintaining their accuracy in real-time as road changes occur is difficult, necessitating a navigation system with a semantic data map mechanism.
Innovation Solution
A navigation system that captures semantic frames from sensor data streams, corrects pose errors, generates semantic grid maps with high-level and low-level grids, calculates grid map statistics, filters semantic points, and generates a semantic map for display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed high definition maps are generated to display every aspect of roadway, then navigation precision is improved, but system complexity increases
Solution Approach 1:
The patent divides the semantic map into two hierarchical grids: a high-level grid for coarse navigation and a low-level grid for detailed navigation. This segmentation allows the system to manage detailed roadway information in an organized manner, improving navigation precision while keeping the system structure manageable through hierarchical organization rather than monolithic complexity.
Solution Approach 2:
The patent introduces a semantic score dimension to evaluate and filter semantic points. By adding this evaluation dimension, the system can distinguish between reliable and unreliable map data, improving navigation precision through data quality filtering without proportionally increasing system complexity.
2Measurement precision
If real-time map updates are implemented to capture road changes, then map accuracy is improved, but processing time increases
Solution Approach 1:
The patent calculates semantic scores for semantic points in advance during the map generation process. This preliminary scoring allows the system to pre-evaluate data quality before real-time updates are needed, enabling fast filtering and processing during dynamic map updates without increasing overall processing time.
Solution Approach 2:
The system uses semantic scores as feedback to dynamically filter and weight semantic points during real-time map updates. This feedback mechanism allows the system to quickly adapt to road changes by relying on pre-scored data quality metrics, improving map accuracy while maintaining efficient processing speeds.
3Reliability
If semantic points are filtered based on grid map statistics, then data quality is improved, but computational load increases
Solution Approach 1:
The patent computes grid map statistics in advance during the map generation phase, storing summary information about semantic point distributions. This preliminary computation allows the system to perform quick filtering during real-time operations using pre-calculated statistics, improving data quality while minimizing computational load during critical processing moments.
Solution Approach 2:
The system creates a simplified representation of map statistics at the high-level grid dimension that can be quickly queried during filtering operations. This statistical copy allows efficient data quality assessment without requiring complex real-time computations, reducing computational load while maintaining high reliability.
Data Source
AI summary
A navigation system includes: a control circuit configured to: a control circuit configured to: capture a semantic frame from a sensor data stream for a region of interest, calculate a score (EQ1) for semantic points in the semantic frame, align the semantic points includes a pose error corrected, generate a semantic grid map of the semantic points including a high-level grid and a low-level grid, calculate grid map statistics by counting the semantic points in the low-level grid within the high-level grid, filter the semantic points in the low-level grid based on the grid map statistics of the high-level grid, and generate a semantic map from the low-level grid filtered and the high-level grid; and a communication circuit, coupled to the control circuit, configured to: process the semantic frame through a network, and process the semantic map through the network for displaying on a device.