Robotic Mapping via Graph Optimization and Scan Matching
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Solution Overview
Problem
Robotic mapping is hindered by environmental noise, sensor noise, inaccuracies, and ambiguities, leading to navigation issues such as getting lost, collisions, or failure to complete tasks, and existing systems are resource-intensive and not suitable for low-cost hardware.
Innovation Solution
The method involves creating a graph of sensor nodes, performing scan matching on extended scan groups, determining confidence levels, and optimizing the graph to generate an accurate map, which can be rendered using ray tracing, allowing for accurate navigation and efficient processing on lower-cost hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional mapping methods are used to improve mapping accuracy, then navigation reliability improves, but processing power requirements increase and hardware cost increases
Solution Approach 1:
The patent segments the mapping problem into discrete graph nodes representing sensor scans at different locations. Each node is processed independently with confidence values calculated based on local scan matching quality, allowing parallel processing and reducing overall computational burden while maintaining navigation reliability.
Solution Approach 2:
The patent changes the parameter representation from continuous sensor data to discrete graph nodes with associated confidence values. This parameter transformation enables efficient optimization algorithms to be applied, reducing processing power requirements while maintaining accurate environment mapping for reliable navigation.
2Measurement precision
If high-precision sensors and processors are used to improve mapping accuracy, then mapping precision improves, but device cost increases
Solution Approach 1:
The patent employs inexpensive sensor scans that are processed through the graph optimization framework to achieve high mapping precision. The method extracts maximum value from low-cost sensors by using intelligent processing algorithms, thereby reducing device cost while maintaining mapping precision suitable for autonomous navigation.
Solution Approach 2:
The patent substitutes expensive high-precision hardware with a computational approach using graph optimization and scan matching algorithms. This software-based solution replaces the need for costly sensors and processors, achieving high mapping precision through intelligent data processing rather than hardware excellence.
3Measurement precision
If detailed environment mapping is performed to improve navigation accuracy, then navigation precision improves, but processing time increases
Solution Approach 1:
The patent performs preliminary scan matching and confidence value calculation for each graph node as scans are collected during robot traversal. This preliminary processing organizes data into an optimized graph structure in advance, reducing the computational burden during actual navigation and allowing detailed mapping without excessive processing delays.
Solution Approach 2:
The patent implements dynamic graph optimization where the mapping process adapts as the robot moves through the environment. Confidence values and node connections are updated in real-time based on new sensor data, enabling progressive building of accurate navigation maps without requiring complete processing of all data at once, thus reducing overall processing time.
Data Source
AI summary
Systems and methods for robotic mapping are disclosed. In some exemplary implementations, a robot can travel in an environment. From travelling in the environment, the robot can create a graph comprising a plurality of nodes, wherein each node corresponds to a scan taken by a sensor of the robot at a location in the environment. In some exemplary implementations, the robot can generate a map of the environment from the graph. In some cases, to facilitate map generation, the robot can constrain the graph to start and end at a substantially similar location. The robot can also perform scan matching on extended scan groups, determined from identifying overlap between scans, to further determine the location of features in a map.