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Updated: Jan 16, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

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Towards Biologically-Inspired Visual SLAM in Dynamic Environments: IPL-SLAM with Instance Segmentation and Point-Line

Jian Liu¹, Donghao Yao¹, Na Liu¹

¹Institute of Machine Intelligence, University of Shanghai for Science and Technology, Shanghai 200093, China.

Biomimetics (Basel, Switzerland)

|September 26, 2025

Summary

This summary is machine-generated.

Instance-level Point-Line SLAM (IPL-SLAM) improves mobile robot navigation in dynamic environments by filtering unreliable features from moving objects. This robust visual SLAM framework enhances localization accuracy and mapping, outperforming existing systems.

Keywords:

biologically-inspired perception dynamic environments instance segmentation point-line feature fusion semantic point cloud visual SLAM

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Area of Science:

Robotics and Computer Vision
Artificial Intelligence and Machine Learning

Background:

Simultaneous Localization and Mapping (SLAM) is crucial for autonomous robots but struggles with dynamic environmental elements.
Moving objects introduce unreliable features, leading to significant localization errors and degraded performance.

Purpose of the Study:

To develop a robust visual SLAM framework, Instance-level Point-Line SLAM (IPL-SLAM), specifically designed for dynamic environments.
To enhance localization accuracy and environmental reconstruction by effectively handling dynamic objects.

Main Methods:

Utilizing YOLOv8 for instance segmentation to identify dynamic regions and create semantic priors.
Extracting both point (ORB) and line features (LSD) for comprehensive environmental representation.
Implementing motion consistency checks and adaptive-weight optimization to filter dynamic features and refine pose estimation.

Main Results:

IPL-SLAM demonstrated superior trajectory accuracy and robustness compared to DS-SLAM and ORB-SLAM2 on the TUM RGB-D dataset.
The framework successfully filters dynamic features, mitigating localization errors in complex indoor scenes.
A static semantic point cloud map was constructed, improving overall scene understanding.

Conclusions:

IPL-SLAM offers a significant advancement in visual SLAM for dynamic environments.
The integration of semantic awareness and geometric cues provides a robust solution for autonomous navigation.
This framework paves the way for more reliable robotic perception and interaction in real-world scenarios.