Waypoint Navigation Map Refinement Using Alternate Traversable Edges
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Solution Overview
Problem
Existing robotic navigation systems face inaccuracies in topological maps due to odometry drift and localization errors, leading to incorrect graph topologies and non-traversable edges, which hinder efficient navigation and localization.
Innovation Solution
A method and system that utilize sensor data to verify and update topological maps by identifying candidate alternate edges and confirming them as traversable paths, refining the map to ensure consistency and accuracy, using algorithms like iterative closest point and sparse nonlinear optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional odometry-based topological mapping is used, then the robot can build a navigation graph, but the map accuracy deteriorates due to odometry drift and localization errors
Solution Approach 1:
The system uses sensor data (LIDAR, cameras, depth sensors) to continuously verify and validate the topological map and navigation graph. By comparing actual sensor observations with the stored map data, the system detects drift and localization errors, then corrects the graph topology and edge traversability information accordingly, ensuring both accuracy and reliability
Solution Approach 2:
The system performs preliminary validation of candidate edges by checking sensor data before finalizing the topological map. By proactively identifying and verifying potential navigation paths using multiple sensors before committing them to the graph structure, the system prevents accumulation of odometry errors and ensures map accuracy from the outset
2Measurement precision
If the topological map is refined with more detailed edges and waypoints, then navigation accuracy improves, but the computational complexity and processing time increase
Solution Approach 1:
The system segments the environment into discrete waypoints and edges, creating a topological representation that balances detail with computational efficiency. By dividing the continuous space into manageable graph elements and only processing relevant segments with sensor validation, the system achieves high navigation accuracy without overwhelming computational complexity
Solution Approach 2:
The system applies different levels of detail and validation to different parts of the navigation graph based on local requirements. High-traffic or critical paths receive more rigorous sensor validation and detail, while less critical areas use simplified representations, optimizing the balance between accuracy and processing complexity
3Adaptability or versatility
If the robot explores more alternate paths to verify traversability, then the completeness of the navigation graph improves, but the time and computational resources required increase
Solution Approach 1:
The system performs partial verification of alternate paths by using sensor data to check traversability of candidate edges without requiring complete exploration of every possible path. By validating a sufficient subset of alternate routes using LIDAR and depth sensors to confirm navigability, the system achieves path completeness while limiting time expenditure through intelligent sampling rather than exhaustive exploration
Data Source
AI summary
The operations of a computer-implemented method include obtaining a topological map of an environment including a series of waypoints and a series of edges. Each edge topologically connects a corresponding pair of adjacent waypoints. The edges represent traversable routes for a robot. The operations include determining, using the topological map and sensor data captured by the robot, one or more candidate alternate edges. Each candidate alternate edge potentially connects a corresponding pair of waypoints that are not connected by one of the edges. For each respective candidate alternate edge, the operations include determining, using the sensor data, whether the robot can traverse the respective candidate alternate edge without colliding with an obstacle and, when the robot can traverse the respective candidate alternate edge, confirming the respective candidate alternate edge as a respective alternate edge. The operations include updating, using nonlinear optimization and the confirmed alternate edges, the topological map.