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Human Collaborative Localization and Mapping in Indoor Environments with Non-Continuous Stereo.

Edmundo Guerra1, Rodrigo Munguia2, Yolanda Bolea3

  • 1Department of Automatic Control, Technical University of Catalonia (UPC), Barcelona 08034, Spain. edmundo.guerra@upc.edu.

Sensors (Basel, Switzerland)
|March 2, 2016
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Summary
This summary is machine-generated.

This study introduces a novel monocular simultaneous localization and mapping (SLAM) approach using wearable sensors and an Extended Kalman Filter (EKF). It enhances depth estimation through a pseudo-stereo method for improved robot navigation.

Keywords:
HRIcollaborative roboticsindoor mappingmonocular SLAM

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Area of Science:

  • Robotics
  • Computer Vision
  • Sensor Fusion

Background:

  • Monocular Simultaneous Localization and Mapping (SLAM) is crucial for robot navigation but faces challenges with depth estimation.
  • Human-Robot Interaction (HRI) paradigms offer opportunities for collaborative sensing and improved SLAM.
  • Wearable sensors can augment robotic perception, but their integration requires sophisticated fusion techniques.

Purpose of the Study:

  • To develop an enhanced monocular SLAM system by fusing data from wearable bearing-only sensors.
  • To improve depth estimation for monocular sensors within a collaborative HRI context.
  • To address challenges in SLAM, such as singular trajectories and dynamic movements.

Main Methods:

  • Full fusion of bearing-only sensor data into an Extended Kalman Filter (EKF).
  • Delayed Inverse-Depth (DI-D) SLAM framework incorporating human-view camera data.
  • Geometrical modeling for view overlap prediction and activation of a pseudo-stereo methodology.
  • Stochastic triangulation of SIFT/SURF matched points for depth measurement.

Main Results:

  • Experimental validation using real-world indoor trajectory data.
  • Demonstrated enhancement of depth estimation for the robotic monocular sensor.
  • Successful mapping and localization of a wearable device in challenging scenarios.
  • Effective handling of singular trajectories and rapid turns.

Conclusions:

  • The proposed approach significantly improves monocular SLAM performance by leveraging wearable sensor data.
  • The integration of HRI and collaborative sensing enhances depth perception and localization accuracy.
  • The pseudo-stereo methodology provides real-time depth measurement capabilities.