SLAM system for mobile robots using fused vision and motion sensor data

SLAM System for Mobile Robots Using Fused Vision and Motion Sensor Data

This technology is a SLAM system for mobile robots that improves localization accuracy by combining environmental feature points and spatial occupancy data extracted from vision sensors with movement data calculated from motion sensors using a probability-based data fusion technique.

When using vision sensors alone, issues such as motion blur during robot movement and decreased accuracy in dynamic environments often lead to cumulative errors in localization and mapping.

This technology proposes a modular SLAM architecture that includes a vision sensor processor, a motion sensor processor, and a third processor that re-estimates the robot's position by fusing this information using probability-based filters such as a Kalman filter. This allows for mutual compensation between sensors, suppressing error accumulation and enabling the creation of precise maps. It can be applied to various indoor autonomous driving applications, including cleaning robots, logistics robots, and service robots, significantly increasing the reliability of localization through sensor fusion.

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Key Features:

• Vision sensor including at least one of the following: monocular camera, stereo camera, laser sensor, or ultrasonic sensor
• Motion sensor including an accelerometer, gyroscope, encoder, etc., to acquire robot motion data and provide it for movement calculation
• Processor that estimates position using vision sensor data, calculates movement using motion data, and re-estimates position by fusing the two
• Mapping device that creates maps using the estimated position and feature point or spatial occupancy information