Map Construction Method for Autonomous Navigation
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Solution Overview
Problem
Existing map construction methods for autonomous navigation devices are limited by sensor type, as they require customization and have low adaptability across different sensors, making it difficult to construct interchangeable maps using data from various sensors.
Innovation Solution
A method that extracts feature parameters from feature objects and adjusts the point cloud density of non-feature objects to a preset value, allowing for the construction of maps that can be used by different sensors, achieving data homogenization and enabling positioning solutions without sensor category limitations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing map construction methods are customized for specific sensor categories, then the construction process is optimized for that sensor type, but the adaptability to different sensor types is reduced
Solution Approach 1:
The patent creates a universal map construction method that can process data from multiple sensor types (laser radar, ultrasonic sensor, camera) through a unified point cloud representation. The system converts different sensor data formats into a common point cloud structure with standardized attributes (position, intensity, time stamp), enabling the same map construction algorithm to work across different sensor categories without requiring separate customization for each sensor type.
Solution Approach 2:
The patent transforms heterogeneous sensor data into a unified parameter set by converting various sensor outputs into standardized point cloud parameters including three-dimensional position coordinates (x, y, z), intensity values, and time stamps. This parameter standardization allows different sensor types to be processed through the same map construction pipeline while preserving the essential characteristics of each sensor's data.
2Reliability
If heterogeneous sensor data is processed without homogenization, then the original sensor-specific characteristics are preserved, but the map construction complexity increases and interchangeability is reduced
Solution Approach 1:
The patent applies homogeneity by converting diverse sensor data from different categories into a unified point cloud format with consistent structure and parameters. All sensor data (laser radar, ultrasonic, camera) are transformed into points with standardized attributes including spatial coordinates, intensity, and temporal information, creating a homogeneous data representation that simplifies subsequent map construction processing while maintaining the essential characteristics of the original sensor measurements.
3Measurement precision
If point cloud density varies across different objects, then the original data resolution is maintained, but the map construction efficiency and consistency are reduced
Solution Approach 1:
The patent implements partial action by selectively adjusting point cloud density only for non-feature objects while preserving the original high-resolution point cloud data for feature objects. This approach maintains measurement precision for critical features (such as road markings, traffic signs, and obstacles) that require detailed representation, while applying density adjustment to background elements to improve overall map construction efficiency and consistency.
Data Source
AI summary
A map construction method, an electronic device and a readable storage medium are disclosed. The map construction method includes: obtaining three-dimensional point cloud data corresponding to observed objects in current target frame data, the observed objects including a feature object and a non-feature object; obtaining a feature parameter corresponding to the feature object based on three-dimensional point cloud data corresponding to the feature object, the feature parameter being used to indicate a position and a size of the feature object; and adjusting a point cloud density of three-dimensional point cloud data corresponding to the non-feature object to a preset value, to construct a map according to the feature parameter and the three-dimensional point cloud data of which the point cloud density is adjusted.