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Multi-View Fusion-Based 3D Object Detection for Robot Indoor Scene Perception.

Li Wang1, Ruifeng Li2, Jingwen Sun3

  • 1State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China. 15b908017@hit.edu.cn.

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|September 25, 2019
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Summary
This summary is machine-generated.

This study introduces a novel two-stage 3D object detection algorithm for service robots. It enhances 3D scene perception in cluttered environments by fusing multi-view point clouds and filtering detections for improved accuracy.

Keywords:
3D object detectionManhattan framemulti-view fusionsemantic segmentation

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Service robots require robust 3D scene perception for autonomous operation in cluttered indoor environments.
  • Existing 3D object detection methods face challenges like incomplete observations, duplicate detections, and object intersections.

Purpose of the Study:

  • To develop a two-stage 3D object detection algorithm that addresses the limitations of single-view detection in cluttered environments.
  • To improve the accuracy and reliability of 3D object detection for service robots.

Main Methods:

  • A two-stage approach involving multi-view 3D object point cloud fusion and post-detection filtering.
  • Utilizing 2D semantic segmentation and an unsupervised Locally Convex Connected Patches (LCCP) method for object segmentation.
  • Employing Manhattan Frame estimation for orientation calculation and 3D bounding box generation.
  • Implementing an object database with a fusion criterion for multi-view object consolidation.
  • Applying prior knowledge-based filtering to remove incorrect and intersecting object detections.

Main Results:

  • The proposed algorithm successfully fuses multi-view data to achieve more accurate 3D object bounding boxes.
  • The LCCP method effectively segments objects from background clutter.
  • Manhattan Frame estimation provides reliable object orientation.
  • The filtering approach successfully removes erroneous and overlapping detections.
  • Experimental validation on SceneNN and real-world data demonstrates stability and accuracy.

Conclusions:

  • The developed two-stage 3D object detection algorithm significantly enhances a service robot's ability to perceive and interact with objects in cluttered indoor settings.
  • Multi-view fusion and intelligent filtering are crucial for robust 3D object detection in complex environments.
  • The algorithm provides a stable and accurate solution for 3D semantic segmentation and bounding box detection.