Research on UAV Perception Systems for Dynamic Indoor Environments: PointNet++ Optimization with Hierarchical Contextual Attention

Yuhan Jiang

doi:10.47297/taposatWSP2633-456915.20250605

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Research on UAV Perception Systems for Dynamic Indoor Environments: PointNet++ Optimization with Hierarchical Contextual Attention

Volume 6, Issue 5

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Research on UAV Perception Systems for Dynamic Indoor Environments: PointNet++ Optimization with Hierarchical Contextual Attention
Research on UAV Perception Systems for Dynamic Indoor Environments: PointNet++ Optimization with Hierarchical Contextual Attention
Department of Mechanical Engineering, City University of Hong Kong, Hong Kong, 999077, China
Vol. 6, Issue 5, Pages: 68-73(2025)
Published：19 May 2025
DOI：10.47297/taposatWSP2633-456915.20250605
Accepted：

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Jiang Yuhan.Research on UAV Perception Systems for Dynamic Indoor Environments: PointNet++ Optimization with Hierarchical Contextual Attention[J].Theory and Practice of Science and Technology,2025,06(05):68-73.
DOI：

Jiang Yuhan.Research on UAV Perception Systems for Dynamic Indoor Environments: PointNet++ Optimization with Hierarchical Contextual Attention[J].Theory and Practice of Science and Technology,2025,06(05):68-73. DOI： 10.47297/taposatWSP2633-456915.20250605.

摘要

Abstract

This research addresses the challenges of dynamic perception systems for UAVs in indoor environments by proposing an optimization method for PointNet++ that integrates hierarchical contextual attention mechanisms. To address the issue of imprecise boundary feature extraction in traditional LiDAR point cloud processing for dynamic object detection and tracking

we designed a Hierarchical Contextual Attention Feature Propagation (HCAFP) module. This module establishes weighted connections between high-level semantic features and low-level geometric features

significantly enhancing the model's perception capabilities for object part boundaries. Experimental results show that the enhanced model achieved an instance average IoU of 86.7% on the ShapeNetCore dataset

an improvement of 1.3 percentage points over the original PointNet++ MSG architecture. Additionally

we proposed a series of model optimization strategies

including model compression and quantization

lightweight model structure design

and hierarchical sparse attention mechanisms

enabling the improved model to be efficiently deployed on UAV platforms with limited computational resources while maintaining competitive performance (85.3% instance average IoU). This research provides a new technical approach for UAV autonomous navigation and dynamic object perception in indoor environments

with significant implications for UAV applications in complex dynamic indoor settings.

关键词

Keywords

references

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