3D Point-Cloud Vehicle Attitude Estimation for Autonomous Loading
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Solution Overview
Problem
In unmanned electric shovel operations, accurately estimating the attitude of vehicles, such as mining trucks, is challenging due to the need for precise positioning and orientation for autonomous loading, which existing technologies have not adequately addressed.
Innovation Solution
A vehicle attitude estimation method using point cloud data from Lidar scans to identify target surfaces and corners, employing bounding box estimation and region growth algorithms to determine the vehicle's attitude, ensuring accurate geometric information-based estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional attitude estimation methods are used, then the system complexity is low, but the measurement precision of vehicle attitude is insufficient
Solution Approach 1:
The patent transitions from traditional 2D image-based attitude estimation to 3D point cloud data processing. By utilizing three-dimensional spatial information from Lidar scans, the system achieves more accurate vehicle attitude estimation through volumetric analysis and 3D geometric feature extraction, resolving the contradiction between measurement precision and device complexity.
Solution Approach 2:
The patent creates a virtual bounding box model that copies and represents the physical vehicle's spatial envelope. This virtual model is constructed from point cloud data and used to estimate vehicle attitude, replacing direct physical measurement instruments while achieving high precision through computational geometry.
2Measurement precision
If point cloud data processing is used, then the measurement precision of vehicle attitude is improved, but the loss of time for data processing increases
Solution Approach 1:
The patent extracts only the essential geometric features (bounding box vertices and edges) from the complete point cloud data. By focusing on the minimal necessary information rather than processing all points, the system achieves accurate attitude estimation while significantly reducing data processing time and computational load.
Solution Approach 2:
The patent applies partial action by processing only the subset of point cloud data that contains vehicle-related information, excluding background and irrelevant points. This selective processing approach maintains measurement precision while reducing the overall data processing time through intelligent data filtering and feature selection.
Data Source
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AI summary
The present disclosure provides a vehicle attitude estimation method and apparatus, an electronic device and a storage medium, relates to a technical field of data processing, and in particular to fields of automatic driving, intelligent transportation, Internet of Things, big data and the like. A specific implementation solution includes: obtaining (S101) first target data, based on point cloud data of a vehicle, the first target data being capable of constituting a target surface of the vehicle; performing (S102) attitude estimation on a target body for surrounding the vehicle, based on the first target data, to obtain an estimation result; and estimating (S103) an attitude of the vehicle, based on the estimation result. According to the present disclosure, precise or accurate estimation of the attitude of the vehicle may be achieved.