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Accurate and Robust Monocular SLAM with Omnidirectional Cameras.

Shuoyuan Liu1, Peng Guo2, Lihui Feng3

  • 1The Key Laboratory of Photonics Information Technology, Ministry of Industry and Information Technology, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100086, China. lsyzge405@163.com.

Sensors (Basel, Switzerland)
|October 19, 2019
PubMed
Summary

This study enhances Simultaneous Localization and Mapping (SLAM) using omnidirectional cameras, improving accuracy and robustness for applications like autonomous driving. The new system effectively utilizes wide-angle images, achieving low positioning errors in diverse environments.

Keywords:
fisheye camerasmap initializationomnidirectional camerassimultaneous localization and mappingvisual SLAM

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Simultaneous Localization and Mapping (SLAM) is crucial for autonomous systems.
  • Existing SLAM methods often struggle with wide-angle or omnidirectional cameras due to image distortion.
  • The ORB-SLAM framework provides a robust foundation for visual SLAM.

Purpose of the Study:

  • To develop an improved monocular visual SLAM system utilizing omnidirectional cameras.
  • To enhance the ORB-SLAM framework to accommodate the unique properties of wide-angle and fisheye lenses.
  • To improve the accuracy and robustness of SLAM systems in environments with significant image distortion.

Main Methods:

  • Extended the ORB-SLAM framework with an enhanced unified camera model for projection.
  • Implemented a novel map initialization method specifically for omnidirectional cameras.
  • Analytically derived Jacobian matrices for reprojection errors concerning camera pose and 3D point positions.

Main Results:

  • The system demonstrated real-time performance.
  • Achieved positioning errors below 0.1% in small indoor environments and under 1.5% in large environments.
  • Showcased increased accuracy and robustness compared to traditional pinhole model-based SLAM systems.

Conclusions:

  • The proposed SLAM system effectively leverages omnidirectional cameras, even with extreme distortion.
  • The enhanced unified camera model and initialization method improve SLAM performance with wide-angle lenses.
  • This work offers a more accurate and robust SLAM solution for applications requiring a wider field of view.