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Shaped-Based Tightly Coupled IMU/Camera Object-Level SLAM.

Ilyar Asl Sabbaghian Hokmabadi1, Mengchi Ai1, Naser El-Sheimy1

  • 1Department of Geomatics Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada.

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Summary
This summary is machine-generated.

This study introduces a novel object-level simultaneous localization and mapping (SLAM) method using Rao-Blackwellized Particle Filtering (RBPF). It achieves accurate robot positioning by utilizing object shape and undelayed initialization, overcoming limitations of Gaussian error assumptions and motion model drift.

Keywords:
IMU/camera fusionRBPF-SLAMcoarse-to-fine pose estimationobject-level SLAMshape-based pose estimationtightly coupledundelayed initialization

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Object-level Simultaneous Localization and Mapping (SLAM) is crucial for robot navigation and interaction.
  • Existing SLAM methods often rely on Gaussian error assumptions and delayed object initialization, leading to accumulated errors.
  • Current approaches depend on surface features, limiting their use on textureless objects.

Purpose of the Study:

  • To develop an accurate object-level SLAM solution that overcomes limitations of existing methods.
  • To improve robot localization accuracy by utilizing object shape and undelayed initialization.
  • To provide a robust SLAM solution applicable to objects lacking surface texture.

Main Methods:

  • Implementation of Rao-Blackwellized Particle Filtering (RBPF) for SLAM.
  • Development of a tightly coupled Inertial Measurement Unit (IMU)/camera system.
  • Undelayed initialization of objects within the map, leveraging object shape rather than surface features.

Main Results:

  • Achieved position errors ranging from 4.1 to 13.1 cm (0.005 to 0.021 of the total path).
  • The RBPF approach does not assume predefined error distributions for parameters.
  • The shape-based method enables SLAM for textureless objects.

Conclusions:

  • The proposed object-level SLAM method offers improved accuracy and robustness.
  • The RBPF framework provides a flexible approach to error distribution in SLAM.
  • This research advances robot perception and navigation capabilities, particularly for challenging environments with textureless objects.