Localization methods for mobile robots

Mobile Robot Localization and Recovery Technology Using Area-Based Matching Error

This technology estimates a mobile robot's position by calculating matching errors based on the area deviation between range sensor scan data and environment map-based predicted data, applying these to a probability density function, and enabling rapid recovery in the event of a failure.

Existing beam models are sensitive to sensor data errors, suffer from reduced localization accuracy in real-world environments with unmapped obstacles, and experience delays in recovery when localization fails due to issues like wheel slippage.

This technology calculates matching errors using the deviation between scan range areas and predicted range areas, removes noise via a median filter, detects failures using statistical thresholds, and proposes a method for probabilistic re-estimation within a maximum motion boundary. It can be applied to indoor service robots and logistics robots, minimizing downtime by quickly recovering even if the robot loses its position.

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Key Features:

• Acquiring scan data of the surrounding environment based on scans from a range sensor mounted on a mobile robot
• Calculating matching errors based on the deviation between the scan range area and the predicted range area derived from an environment map
• Determining whether localization has failed based on whether the matching error exceeds an error threshold
• Configured to probabilistically re-estimate the position within a maximum motion boundary upon detecting a failure

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This invention was developed with support from the Korea Institute for Advancement of Technology (KIAT) through the project for fostering demand-oriented global leaders in indoor/outdoor robot autonomous driving technology.