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Rts-slam: A Trajectory Consistency-driven Multi-constraint Dynamic Feature-rejection Method For Visual Slam In Dynamic Environments.

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Rts-slam: A Trajectory Consistency-driven Multi-constraint Dynamic Feature-rejection Method For Visual Slam In Dynamic Environments.

Related Experiment Video

An Emerging Target Paradigm to Evoke Fast Visuomotor Responses on Human Upper Limb Muscles

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Published on: August 25, 2020

RTS-SLAM: A Trajectory Consistency-Driven Multi-Constraint Dynamic Feature-Rejection Method for Visual SLAM in

Huailiang Wang¹, Qiming Hu¹, Beicheng Li¹

¹School of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.

Sensors (Basel, Switzerland)

|May 13, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces RTS-SLAM, a real-time semantic visual SLAM system for dynamic environments. It significantly improves localization accuracy by rejecting dynamic features and refining maps, outperforming ORB-SLAM2.

Keywords:

dynamic environment object detection trajectory consistency constraints visual simultaneous localization and mapping (SLAM)

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Published on: August 25, 2020

Area of Science:

Robotics
Computer Vision
Artificial Intelligence

Background:

Simultaneous Localization and Mapping (SLAM) is crucial for autonomous navigation.
Traditional SLAM methods struggle in dynamic environments due to reliance on environmental rigidity.
Dynamic environments cause significant accuracy degradation in conventional SLAM systems.

Purpose of the Study:

To develop RTS-SLAM, a real-time semantic visual SLAM system specifically for dynamic environments.
To enhance localization accuracy and mapping efficiency in the presence of environmental changes.
To overcome the limitations of existing SLAM approaches in non-static settings.

Main Methods:

RTS-SLAM builds upon the ORB-SLAM2 framework, incorporating a multi-layer, constraint-driven dynamic feature-rejection strategy.

Dynamic features are removed using semantic information and geometric constraints, followed by trajectory-consistency analysis.

A dense mapping strategy with global sparsification and critical region refinement reduces memory usage while preserving essential geometries.

Main Results:

RTS-SLAM demonstrated a reduction in average absolute trajectory error by over 95% compared to ORB-SLAM2 on dynamic datasets (TUM RGB-D and Bonn).
The system maintains real-time performance, crucial for practical autonomous navigation.
High localization accuracy was achieved even in challenging dynamic environments.

Conclusions:

RTS-SLAM effectively addresses the challenges of SLAM in dynamic environments.
The proposed feature rejection and mapping strategies significantly improve accuracy and efficiency.
RTS-SLAM offers a robust solution for real-time autonomous navigation in complex, changing surroundings.