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HDID: a method for removing dynamic objects in the 3D point cloud map.

Pengfei Jiang, Xiaoming Wang, Boxue Bi

|March 17, 2026

Summary

This summary is machine-generated.

This study introduces a new method to remove dynamic objects from 3D point cloud maps, improving robot localization and navigation. The technique uses height-density occupancy descriptors and density ratios for accurate map creation.

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Area of Science:

Robotics
Computer Vision
Geospatial Data

Background:

3D point cloud maps are essential for robot localization and navigation.
Dynamic objects in environments cause distortions and artifacts in 3D maps, impacting robot performance.
Existing methods struggle to effectively remove dynamic objects without compromising map integrity.

Purpose of the Study:

To propose a novel method for effectively removing dynamic objects from 3D point cloud maps.
To enhance the accuracy and reliability of 3D maps for autonomous systems.
To overcome limitations of current dynamic object removal techniques.

Main Methods:

Introduction of a height-density occupancy descriptor to represent map height information.
Development of strategies including density ratio test, inner loop detection, and density ratio deletion.
Utilizing quantity of point clouds in upper and lower layers for height representation.

Main Results:

The proposed method effectively removes dynamic objects from 3D point cloud maps.
Static objects are accurately preserved, unlike in some existing approaches.
Demonstrated superior performance in dynamic object removal compared to state-of-the-art methods on open-source datasets.

Conclusions:

The novel method significantly improves the quality of 3D point cloud maps by removing dynamic objects.
This advancement is crucial for enhancing the robustness of robot localization and navigation systems.
The approach offers a more reliable solution for creating accurate 3D environment representations.