Autonomous Ground Surface Modeling for Real-Time Motion Planning
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Solution Overview
Problem
Autonomous systems, such as vehicles and robots, face challenges in creating accurate environmental models in real-time due to latency and processing limitations, which can lead to inaccurate or dangerous operation.
Innovation Solution
The use of a suite of sensors, including cameras and LIDAR, combined with machine learning techniques, allows for the generation of environmental models that characterize surfaces and objects, enabling effective path planning and obstacle avoidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If substantial computer processing is applied to reduce sensor noise and achieve accurate environmental modeling, then measurement precision improves, but device complexity and processing capability requirements worsen
Solution Approach 1:
The patent segments the complex environmental modeling task into distinct processing stages: sensor data acquisition, initial filtering, feature extraction, and model generation. Each stage processes only relevant data with appropriate algorithms, reducing overall computational complexity while maintaining accuracy.
Solution Approach 2:
The system applies processing only to the extent necessary for safe autonomous operation. It processes sensor data to the degree needed to create accurate environmental models without over-processing, balancing computational resources with safety requirements through selective application of filtering and modeling algorithms.
2Reliability
If real-time environmental modeling is implemented for safe autonomous operation, then reliability improves, but processing capability requirements worsen
Solution Approach 1:
The system performs preliminary filtering and preprocessing of sensor data before full environmental modeling. By pre-processing data to remove obvious noise and organize relevant information, the system reduces the computational burden of real-time modeling while ensuring reliable safe operation.
Solution Approach 2:
The patent introduces intermediate data structures and processing layers between raw sensor input and final environmental models. These intermediaries organize and pre-process information, making the data more suitable for efficient real-time modeling and reducing the direct computational burden on the main processing system.
3Device complexity
If minimal computational resources are used, then device complexity reduces, but measurement precision and modeling accuracy worsen
Solution Approach 1:
The system extracts only the essential features and data elements needed for accurate environmental modeling, discarding redundant information. By taking out only the critical components required for safety and accuracy, the system achieves good modeling precision with minimal computational resources.
Solution Approach 2:
The patent dynamically adjusts processing parameters such as filter thresholds, model complexity levels, and data sampling rates based on operational context. This allows the system to maintain adequate modeling accuracy while using minimal computational resources by adapting parameters to the specific situation rather than always using maximum processing.
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
This approach enables autonomous systems to navigate complex terrains safely and efficiently, using minimal computational resources and improving the accuracy of motion planning.
Implementation Method 1
The use of a suite of sensors, including cameras and LIDAR, combined with machine learning techniques, allows for the generation of environmental models
Data Source
AI summary
A method and apparatus for modeling the environment proximate an autonomous system. The method and apparatus accesses vision data, assigns semantic labels to points in the vision data, processes points that are identified as being a drivable surface (ground) and performs an optimization over the identified points to form a surface model. The model is subsequently used for detecting objects, planning, and mapping.