Neural Network Semantic Map Correction via Simulation
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Solution Overview
Problem
Existing technologies rely heavily on manual operations and mapping experts to correct and update semantic grid maps, which are prone to errors due to sensor inaccuracies and ambiguities, leading to increased costs and efforts in maintaining accurate maps.
Innovation Solution
A system and method that utilize a neural network to learn how to build and update large-scale consistent semantic grid maps by simulating a robot's movement in a generated environment, comparing actual and predicted geographical information, and correcting flaws without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual operations are used to correct and update semantic grid maps, then map accuracy can be maintained, but costs and efforts increase significantly
Solution Approach 1:
The system enables self-service by training the neural network to automatically detect and correct errors in semantic grid maps through simulated robot experiences. The network learns to identify inconsistencies between sensor data and map representations, and autonomously generates corrections without human intervention, thereby maintaining map accuracy while eliminating manual labor costs.
Solution Approach 2:
The patent replaces the mechanical manual correction process with an automated neural network system. Instead of mapping experts manually reviewing and correcting map errors, the system uses a trained neural network that processes sensor data and automatically generates map corrections, substituting human mechanical operations with an automated computational system.
2Stability of the object's composition
If manual expert correction is applied to semantic grid maps, then map consistency is improved, but time consumption increases
Solution Approach 1:
The system applies preliminary action by pre-training the neural network on extensive simulated robot experiences before deployment. During operation, the already-trained network can immediately detect and correct map inconsistencies without requiring manual expert intervention, thereby maintaining map consistency while minimizing time consumption compared to waiting for manual corrections.
Solution Approach 2:
The system implements feedback mechanisms where the neural network continuously compares sensor observations with map representations, identifies inconsistencies, and generates corrections. This closed-loop feedback process automatically maintains map consistency by detecting and correcting errors as they occur, eliminating the time delay associated with manual expert review cycles.
3Ease of manufacture
If semantic grid maps are built using traditional sensor fusion methods, then initial map estimation is obtained, but errors from sensor inaccuracies and ambiguities persist
Solution Approach 1:
The patent introduces an intermediary neural network component that mediates between raw sensor data and the final map representation. The network learns to interpret ambiguous sensor readings and resolve inconsistencies by comparing multiple observations with the map model, thereby improving map reliability while maintaining the ease of automated map building without requiring manual expert intervention.
Data Source
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AI summary
The present invention relates to a system and a method for training a neural network for geographical information, and particularly relates to a system and a method for updating geographical information using the neural network. In accordance with an aspect of the present invention, there is a system for training a neural network for geographical information comprising: a processor operable to generate an environment from actual geographical information, and simulate a robot moving in the environment to obtain first data from the simulation of the robot, characterised in that: the processor is operable to produce corrupted geographical information from the actual geographical information to obtain second data from the corrupted geographical information, and the neural network is operable to construct predicted geographical information based on the first and second data, and compare the actual geographical information and the predicted geographical information to train the neural network.