Lidar 3D Modeling Guided by Camera Object Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicle navigation systems face challenges in efficiently processing lidar return data to create accurate 3D models of surroundings, particularly in occluded areas, due to high computational requirements and limited object classification capabilities using lidar data alone.
Innovation Solution
Integration of a visible light camera for fast and high-quality object classification, which reduces computational load by attributing lidar returns to classified objects, allowing for more accurate and efficient maintenance of a 3D model by correlating camera and lidar data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If lidar data alone is used for object classification and 3D modeling, then measurement precision can be maintained, but device complexity and computational requirements increase significantly
Solution Approach 1:
The system segments the classification task by using the camera to identify and classify objects in 2D image space, then segments the lidar point cloud based on these classifications. This divides the computationally intensive lidar processing into smaller, manageable groups associated with specific object classes, reducing overall computational complexity while maintaining precision.
Solution Approach 2:
The camera serves as an intermediary device that performs preliminary object classification. Its output acts as a mediator that guides and constrains subsequent lidar data processing, enabling the system to achieve high classification accuracy without processing all lidar points individually, thus reducing computational requirements.
2Measurement precision
If all lidar points are processed individually for 3D modeling, then measurement precision is maintained, but productivity decreases due to high computational load
Solution Approach 1:
The system merges multiple lidar points that belong to the same object class into grouped point clouds. By combining points from the same object rather than processing them individually, the system maintains 3D modeling precision while significantly reducing the number of discrete processing operations required, thereby improving productivity.
Solution Approach 2:
The camera performs preliminary object identification and classification before lidar data processing begins. This preliminary action pre-groups potential object candidates, allowing the lidar system to skip individual point analysis for points already classified by the camera, thus accelerating processing while maintaining model accuracy.
3Measurement precision
If camera data is integrated with lidar data for object classification, then object classification accuracy improves, but device complexity increases
Solution Approach 1:
The system implements a universal classification framework where the camera provides broad object category identification that applies to all subsequent lidar processing. This multi-functional approach allows the same camera-based classification logic to be applied across different object types and scenarios, managing integration complexity through a unified methodology.
Solution Approach 2:
The camera output serves as an intermediary layer that translates complex visual recognition results into simplified object class labels. This intermediary representation standardizes the interface between camera and lidar systems, reducing integration complexity by providing a common data format and classification vocabulary that both sensors can work with.
4Productivity
If camera-based classification is used to reduce computational load, then productivity improves, but loss of information may occur in transitioning from 2D to 3D data
Solution Approach 1:
The system uses the camera classification as an intermediary guide rather than a complete replacement for lidar processing. The camera provides 2D object boundaries and classifications, while the lidar supplies complementary 3D depth information. This intermediary approach allows the system to leverage camera efficiency while preserving essential depth data from lidar for accurate 3D modeling.
Solution Approach 2:
The system merges camera-based 2D classification results with lidar-based 3D point cloud data into a unified object representation. By combining these data sources, the system recovers depth information that would be lost in pure 2D processing while maintaining the processing efficiency gains from camera-based preliminary classification.
Data Source
AI summary
Post-processing in a lidar system may be guided by camera information as described herein. In one embodiment, a camera system has a camera to capture images of the scene. An image processor is configured to classify an object in the images from the camera. A lidar system generates a point cloud of the scene and a modeling processor is configured to correlate the classified object to a plurality of points of the point cloud and to model the plurality of points as the classified object over time in a 3D model of the scene.