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Monocular SLAM for autonomous robots with enhanced features initialization.

Edmundo Guerra¹, Rodrigo Munguia², Antoni Grau³

¹Automatic Control Department, Technical University of Catalonia UPC, 08028 Barcelona, Spain. edmundo.guerra@upc.edu.

Sensors (Basel, Switzerland)

|April 5, 2014

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This study enhances monocular SLAM using human-robot interaction (HRI). By leveraging a second camera worn by a human, it improves metric scale initialization and reduces drift in simultaneous localization and mapping.

Area of Science:

Robotics
Computer Vision
Human-Robot Interaction

Background:

Monocular SLAM (Simultaneous Localization and Mapping) traditionally faces challenges with metric scale initialization and drift.
Existing techniques like DI-D SLAM (Delayed Inverse-Depth SLAM) require artificial landmarks for scale, limiting real-world applicability.

Purpose of the Study:

To develop a novel monocular SLAM approach that integrates human-robot interaction (HRI) for improved performance.
To overcome the metric scale initialization problem in monocular SLAM by utilizing a secondary human-worn camera.

Main Methods:

Modified DI-D SLAM incorporating data from a human-worn secondary monocular sensor.
Pseudo-stereo calibration using overlapping fields of view between robot and human cameras for depth estimation via parallax.

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SURF (Speeded Up Robust Features) feature matching for stereo estimation.

Main Results:

Successfully initialized metric scale without artificial landmarks by using human-camera parallax.
Improved feature initialization with reduced uncertainty compared to traditional monocular SLAM.
Demonstrated reduction in scale and orientation drift in real-world experiments.

Conclusions:

The proposed HRI-based approach effectively addresses key limitations of monocular SLAM.
This method offers a more robust and scalable solution for real-time localization and mapping applications.
Integration of human guidance enhances the accuracy and reliability of SLAM systems.