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Linear RGB-D SLAM for Structured Environments.

Kyungdon Joo, Pyojin Kim, Martial Hebert

IEEE Transactions on Pattern Analysis and Machine Intelligence

|August 24, 2021

Summary

This summary is machine-generated.

We introduce L-SLAM, a linear simultaneous localization and mapping (SLAM) method that simplifies camera rotation estimation in structured environments. This approach achieves comparable performance to state-of-the-art methods without complex nonlinear optimization.

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Area of Science:

Robotics
Computer Vision
Artificial Intelligence

Background:

Simultaneous Localization and Mapping (SLAM) is crucial for mobile robots and augmented reality.
Existing SLAM methods often rely on complex nonlinear optimization, limiting efficiency.
Structured environments offer unique regularities that can be exploited for improved SLAM.

Purpose of the Study:

To develop a linear RGB-D SLAM formulation for structured environments.
To reduce the computational complexity of SLAM by exploiting environmental structures.
To enable robust camera tracking and mapping using planar features.

Main Methods:

Utilizing planar features and structural regularities (Manhattan and Atlanta worlds) to decouple camera rotation.
Employing a linear Kalman filter for joint estimation of camera translation and planar landmarks.
Introducing a novel tracking-by-detection scheme for inferring scene structure using Atlanta representation.

Main Results:

L-SLAM achieves comparable performance to state-of-the-art SLAM methods on synthetic and real-world datasets.
The method demonstrates robustness in both Manhattan and Atlanta world scenarios.
Accurate results were observed in augmented reality applications.

Conclusions:

L-SLAM offers an efficient and effective linear approach to RGB-D SLAM in structured environments.
Exploiting scene structure significantly simplifies the SLAM problem.
The proposed method shows promise for real-time applications like augmented reality.