3D Vehicle Environment Mapping with Asynchronous Point Cloud Fusion
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Solution Overview
Problem
Existing systems for generating and updating global tridimensional maps of environments surrounding moving vehicles, especially in convoys, face challenges due to the complexity of combining point clouds from multiple sensors with non-overlapping fields of view and asynchronous data acquisition, which leads to errors and difficulties in maintaining accurate sensor alignment.
Innovation Solution
A method and system where multiple tridimensional sensors generate continuous streams of point cloud frames asynchronously, with a central processing unit aligning and merging these frames into a global cumulated map without the need for synchronized data or overlapping fields of view, using a global coordinate system that adjusts for sensor positions and orientations based solely on the point cloud data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple tridimensional sensors are used to improve map coverage and resolution, then the scope and accuracy of the generated maps are improved, but the complexity of combining point clouds from separated sensors increases due to sparse, noisy and discretized raw data
Solution Approach 1:
The patent introduces a central processing unit as an intermediary that receives point cloud data from multiple distributed sensors, performs coordinate transformations, and generates a unified global map. This mediator handles the complexity of combining sparse, noisy data from separated sensors by centralizing the processing logic and providing a standardized framework for data integration.
Solution Approach 2:
The system employs a universal coordinate system that can accommodate data from multiple sensors with different positions and orientations. The processing unit applies universal transformation algorithms that work regardless of sensor configuration, making the system adaptable to various sensor arrangements while maintaining consistent map generation.
2Area of stationary object
If sensors are mounted on distant vehicles in a convoy to expand coverage, then the environmental monitoring scope is improved, but the field of view of sensors mounted on different vehicles become non-overlapping making combination impossible
Solution Approach 1:
The patent transitions from relying on two-dimensional overlapping fields of view to utilizing three-dimensional spatial coordinates and transformation matrices. By elevating the problem to 3D space with full pose estimation (position and orientation), the system can combine data from sensors with non-overlapping fields of view through coordinate transformations rather than requiring visual overlap.
Solution Approach 2:
The system dynamically adjusts to changing sensor positions and orientations in the convoy. Rather than requiring fixed, pre-calibrated overlapping fields of view, the processing unit continuously transforms and integrates point cloud data based on the current spatial configuration of sensors, allowing adaptability to dynamic convoy movements and non-overlapping scenarios.
3Measurement precision
If sensors are carefully synchronized to enable point cloud comparison and displacement computation, then measurement accuracy is improved, but the system becomes difficult to manage in practice especially when sensors are mounted on distant vehicles
Solution Approach 1:
The patent performs preliminary coordinate transformations and establishes a universal reference frame before combining point cloud data. By pre-defining the transformation framework and processing each sensor's data into the global coordinate system individually, the system avoids the need for complex real-time synchronization and comparison between sensors, simplifying operational management.
4Reliability
If a uniform coordinate system is defined and sensor locations are calibrated to merge measurements reliably, then measurement reliability is improved, but the requirement for fixed and stable sensor positions over time restricts usability to single rigid structures
Solution Approach 1:
The patent implements a dynamic coordinate transformation system that adapts to changing sensor positions and orientations. Rather than requiring fixed sensor mounting, the processing unit continuously transforms point cloud data from each sensor's local coordinate system to the global coordinate system using transformation matrices that account for each sensor's position and orientation, enabling reliable measurement merging on mobile, independently moving vehicles.
Data Source
AI summary
A method and a system for dynamically generating and updating a global tridimensional map of an environment surrounding one or several moving vehicles on which N tridimensional sensors are mounted and communicates with a central processing unit. Each sensor generates a continuous stream of point cloud frames, in parallel and asynchronously with the other sensors, the point cloud frames are representative of object surfaces located in a local volume of the environment surrounding each sensor. The central processing unit continuously receives the continuous streams from the sensors, store them in a memory and, for each newly received point cloud frame of each stream, generates or updates a global cumulated tridimensional map of the environment of the at least one vehicle by determining an aligned point cloud frame in a global coordinate system of the environment, and updating the global cumulated tridimensional map by merging the aligned point cloud frame.