Context-Based Autonomous Perception for Terrain Classification
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Solution Overview
Problem
Model-based classifiers used in autonomous vehicles and UAVs are not robust enough to handle large variations in terrain data due to seasonal changes, weather conditions, and location, requiring frequent retraining and significant processing resources, which limits their scalability and adaptability.
Innovation Solution
A context-based autonomous perception system that uses a contextually-indexed database of labeled reference images, where contextual information such as location, time of day, and weather conditions is used to filter and constrain searches, allowing for more efficient and accurate semantic labeling of terrain features, incorporating three-dimensional point cloud data and various sensors like LIDAR and cameras.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If model-based classifiers are used to identify terrain features in image data, then classification accuracy is improved under nominal conditions, but the system requires frequent retraining and significant processing resources when encountering large degrees of variation in terrain data
Solution Approach 1:
The patent pre-computes and stores feature descriptors for a large database of reference images during an offline phase. This preliminary action allows the system to quickly retrieve and compare features during online operation without requiring intensive real-time processing, thus maintaining high classification accuracy while improving processing efficiency.
Solution Approach 2:
The patent creates feature descriptors that capture the essential characteristics of terrain features and stores them as reference data. Instead of retraining complex models on new data, the system copies and compares these pre-extracted features against new image features, enabling rapid adaptation to varying terrain conditions without significant processing overhead.
2Adaptability or versatility
If model-based classifiers are retrained to incorporate new training data sets, then adaptability to new terrain types and conditions is improved, but the retraining process is time consuming and demands large amounts of processing resources
Solution Approach 1:
The patent performs feature extraction and descriptor computation in advance for a comprehensive database of reference images covering diverse terrain types and conditions. This preliminary action enables the system to adapt to new conditions by retrieving relevant pre-processed features rather than retraining models, significantly reducing adaptation time.
Solution Approach 2:
The patent implements a dynamic feature matching approach where the system can quickly adapt to new terrain conditions by comparing new image features against the existing database of reference features. This dynamic retrieval and matching process allows the system to adapt versatility without the time-consuming retraining required by static model-based approaches.
3Use of energy by moving object
If model-based classifiers are used with limited training data, then processing resources are conserved, but the classifiers are not sufficiently robust to handle a wide range of variability in terrain data
Solution Approach 1:
The patent creates compact feature descriptors that capture essential terrain characteristics and stores them in a reference database. This copying approach allows the system to achieve robustness against terrain variability by comparing new images against diverse reference features without requiring intensive processing resources for model training or complex computations.
Solution Approach 2:
The patent transforms complex image data into simplified feature descriptors that retain the essential variability information needed for robust classification. By changing the parameter representation from full images to condensed feature vectors, the system achieves high robustness with minimal processing resource consumption.
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AI summary
A method of performing context-based autonomous perception is provided. The method includes acquiring perception sensor data as an image by an autonomous perception system that includes a processing system coupled to a perception sensor system. Feature extraction is performed on the image by the autonomous perception system. The feature extraction identifies one or more features in the image. Contextual information associated with one or more conditions present upon acquiring the perception sensor data is determined. One or more labeled reference images are retrieved from at least one of a contextually-indexed database based on the contextual information, a feature-indexed database based on at least one of the features extracted, and a combined contextually- and feature- indexed database. The image is parsed, and one or more semantic labels are transferred from the one or more labeled reference images to form a semantically labeled version of the image.