Multi-Channel Weather Estimation Using Camera-Lidar Alignment
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Solution Overview
Problem
Existing imaging and lidar systems are adversely affected by environmental conditions such as rain, fog, sleet, hail, snow, smoke, and dust, which reduce visibility and inhibit accurate computer vision applications in autonomous vehicles.
Innovation Solution
A method and system that combines camera images and lidar point clouds to identify high- and low-intensity regions, using a model to determine meteorological optical range (eMOR) by aligning and analyzing intensity and distance values, enabling robust estimation of environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If camera and lidar data are integrated to characterize environmental conditions, then measurement precision of weather conditions is improved, but device complexity increases
Solution Approach 1:
The patent combines camera images and lidar point clouds into a unified multi-channel dataset, merging optical intensity information with range data to characterize environmental conditions. This integration allows simultaneous use of multiple sensor types to improve measurement precision while managing system complexity through coordinated processing.
Solution Approach 2:
The system uses a unified environmental condition characterization model that processes both camera and lidar data through the same meteorological optical range (eMOR) estimation framework. This multi-functional approach allows the system to handle diverse sensor inputs using a common processing pipeline, reducing the operational complexity despite multiple sensor types.
2Reliability
If multi-channel data from camera and lidar are processed together, then reliability of environmental condition estimation is improved, but loss of information increases due to data alignment requirements
Solution Approach 1:
The patent performs data alignment and calibration procedures before environmental condition estimation, pre-processing camera and lidar data to establish spatial and temporal correspondence. By performing alignment operations in advance, the system reduces information loss during the actual eMOR estimation process and ensures data consistency across multiple channels.
Solution Approach 2:
The system uses iterative optimization to refine eMOR estimates by comparing predictions with actual multi-channel observations. This feedback mechanism allows the system to correct for minor misalignments and information losses by adjusting estimates based on the combined information from both camera and lidar data.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the accuracy of environmental condition quantification, particularly weather conditions, by integrating camera and lidar data to improve visibility estimation and enhance autonomous vehicle navigation.
Implementation Method 1
a camera operable to capture an image of a first field of view of a surrounding environment from a first perspective
Implementation Method 2
A lidar device may transmit one or more light signals into the surrounding environment (e.g., using one or more emitters) and detect reflections of the one or more light signals (e.g., using one or more detectors) to generate a point cloud
Implementation Method 3
Each point in the point cloud indicates a range between the lidar device and the surrounding environment
Data Source
AI summary
Example embodiments relate to multi-channel dynamic weather estimations. An example embodiment includes a method. The method includes capturing, using a camera, an image of a first field of view of a surrounding environment from a first perspective. The method also includes capturing, using a light detection and ranging (lidar) device, a point cloud of a second field of view of the surrounding environment from a second perspective. Additionally, the method includes aligning, by a computing device, the image with the point cloud. Further, the method includes identifying, by the computing device, one or more high-intensity regions and one or more low-intensity regions of the surrounding environment. In addition, the method includes determining, by the computing device, a first figure of merit that characterizes environmental conditions of the surrounding environment and a second figure of merit that characterizes environmental conditions of the surrounding environment.