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Insect-controlled Robot: A Mobile Robot Platform to Evaluate the Odor-tracking Capability of an Insect

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Research on Mobile Robot Navigation Method Based on Semantic Information.

Ruo-Huai Sun^1,2,3, Xue Zhao⁴, Cheng-Dong Wu^1,3

¹College of Information Science and Engineering, Northeastern University, Shenyang 110819, China.

Sensors (Basel, Switzerland)

|July 13, 2024

Summary

This summary is machine-generated.

This study introduces a semantic laser SLAM system with deep learning and trajectory interpolation for robust mobile robot navigation in dynamic, large-scale environments. The integrated approach enhances mapping accuracy and ensures smooth, stable robot motion.

Keywords:

SLAM occupation probability point cloud semantic laser

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Area of Science:

Robotics
Computer Vision
Artificial Intelligence

Background:

Mobile robot navigation in dynamic, large-scale environments presents significant challenges.
Existing Simultaneous Localization and Mapping (SLAM) systems struggle with dynamic objects and large scenes.
Trajectory planning requires smooth and stable motion for effective robot performance.

Purpose of the Study:

To develop an integrated system for mobile robot navigation and trajectory interpolation in dynamic, large-scale environments.
To enhance the accuracy and robustness of SLAM in complex scenarios.
To ensure smooth and stable robot trajectory execution.

Main Methods:

A semantic laser SLAM system leveraging deep learning (PointNet++) for point cloud semantic segmentation.
Introduction of voxels into occupation probability maps to represent dynamic objects.
Global environment descriptor generation using semantic information and closed-loop global map optimization.
T-trajectory interpolation for motion planning and smooth trajectory transitions.

Main Results:

Improved graph-building and loop-closure effects in large-scale campus environments.
Effective extraction of deep features from dynamic point clouds.
Enhanced representation of dynamic objects within local voxel maps.
Demonstrated vibration-free and stable transitions between target points using T-trajectory interpolation.

Conclusions:

The proposed semantic laser SLAM system combined with deep learning and trajectory interpolation offers a robust solution for mobile robot navigation.
The integration significantly improves mapping performance in dynamic, large-scale environments.
T-trajectory interpolation effectively enhances robot motion performance and trajectory stability.