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A Variable Radius Side Window Direct SLAM Method Based on Semantic Information.

Yan Chen¹, Jianjun Ni^1,2, Emmanuel Mutabazi¹

¹College of Internet of Things Engineering, Hohai University, Changzhou 213022, China.

Computational Intelligence and Neuroscience

|September 1, 2022

Summary

This summary is machine-generated.

This study introduces an improved direct sparse odometry with loop closure (LDSO) for visual simultaneous localization and mapping (SLAM). The novel method enhances robustness against camera imaging disturbances, outperforming existing direct SLAM techniques.

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Area of Science:

Robotics
Computer Vision
Artificial Intelligence

Background:

Simultaneous Localization and Mapping (SLAM) is crucial for mobile robots.
Direct SLAM methods are suitable for certain scenes but lack robustness compared to feature-based methods.

Purpose of the Study:

To propose an improved direct sparse odometry with loop closure (LDSO) to enhance SLAM system robustness.
To focus on the performance of SLAM systems under various camera imaging disturbances.

Main Methods:

A side window strategy with a multilayer stacked pixel blender for image preprocessing.
A variable radius side window strategy incorporating semantic information to down-weight semistatic objects, reducing computation and improving accuracy.

Main Results:

The proposed LDSO method demonstrates superior robustness compared to state-of-the-art direct SLAM methods.
Experiments on KITTI and TUM RGB-D datasets validate performance under diverse imaging disturbances.
Real-world experiments confirm the effectiveness of the proposed approach.

Conclusions:

The improved LDSO offers enhanced robustness for direct SLAM systems facing camera imaging disturbances.
The semantic-based weighting strategy effectively improves computational efficiency and accuracy.
The method proves effective in real-world applications.