Mobile Agent SLAM Mapping with Manhattan Orientation Correction
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Solution Overview
Problem
Conventional SLAM algorithms for mobile agents suffer from orientation drift due to measurement errors, especially in environments without direct orientation measurements, such as buildings or areas with external magnetic field interference.
Innovation Solution
Integration of the Manhattan world concept into a graph-based SLAM algorithm, which includes Manhattan nodes and edges to correct pose estimation by aligning the reference coordinate system with the Manhattan orientation, using sensor data to detect and validate the Manhattan orientation in each acquisition cycle, and applying Manhattan normalization to determine the dominant orientation of rectilinear structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional SLAM algorithms are used for pose estimation, then the system can operate without direct orientation measurements, but orientation drift accumulates due to measurement errors
Solution Approach 1:
The patent introduces Manhattan orientation as an intermediary constraint that mediates between sensor measurements and pose estimation. By assuming walls are aligned with cardinal directions (North-South-East-West), the system uses this intermediate reference frame to correct orientation drift without requiring direct orientation sensors like compasses.
Solution Approach 2:
The patent changes the parameter space by introducing Manhattan orientation angles as additional parameters to be estimated. Instead of directly estimating absolute orientation, the system estimates the deviation from Manhattan alignment, which serves as a constraint to reduce orientation drift in the pose estimation process.
2Measurement precision
If Manhattan world assumption is applied to correct orientation drift, then pose estimation accuracy improves, but the system requires environments with rectilinear structures
Solution Approach 1:
The patent applies partial Manhattan world assumption by using only the extent to which walls align with cardinal directions. The system calculates a Manhattan alignment metric and applies constraints proportionally, allowing it to function partially in non-ideal environments rather than requiring perfect Manhattan structures.
Solution Approach 2:
The patent applies Manhattan constraints locally to wall segments that exhibit rectilinear characteristics rather than requiring the entire environment to conform to Manhattan geometry. The system identifies local regions with strong Manhattan alignment and applies constraints selectively in those regions.
Data Source
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AI summary
The invention relates to a computer-implemented method for operating a mobile agent (1) in a surrounding area on the basis of a pose of the mobile agent (1), wherein a localisation of the mobile agent (1) is carried out for the operation of the mobile agent (1), with the following steps: - capturing (S1) sensor data relating to walls (2) and/or objects (3) located in a surrounding area, the sensor data specifying the alignment and distance of the walls (2) and/or objects (3) in an agent-fixed agent coordinate system (A); - determining (S11-S18) the pose of the mobile agent (1) in the surrounding area with the aid of a SLAM algorithm taking into account a Manhattan orientation.