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Topographic Surveying and Contours01:29

Topographic Surveying and Contours

Topographic surveying is critical for documenting the Earth's surface, focusing on capturing elevations, slopes, and natural and man-made features. It is essential in construction planning, water resource management, and land-use analysis. The primary outcome of such surveys is a topographic map, which uses contour lines to visually represent the shape and slope of the terrain, providing valuable insights into the landscape's characteristics.Contour lines are fundamental to understanding the...
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Related Experiment Video

Updated: Jun 27, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
08:16

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

ORACLE: Object-Centric Autonomous Coverage Exploration Planner for Discrete Trunk Inspection Under Canopy.

Juqi Wei1, Hai Wang1

  • 1School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China.

Sensors (Basel, Switzerland)
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

Autonomous drones now use target-guided exploration for inspecting obstacles like trees. This new Object-centric Autonomous Coverage Exploration (ORACLE) framework significantly improves coverage completeness and reduces mission overhead in forests.

Keywords:
ATSPSOPUAV inspectionautonomous explorationcoverage path planningobject-centric planning

Related Experiment Videos

Last Updated: Jun 27, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
08:16

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Vision

Background:

  • Autonomous Unmanned Aerial Vehicles (UAVs) are crucial for inspecting discrete obstacles in complex environments like forests.
  • Current space-guided exploration methods are inefficient, leading to incomplete coverage and redundant paths by ignoring target locations.

Purpose of the Study:

  • To introduce ORACLE, an Object-centric Autonomous Coverage Exploration framework for efficient UAV-based obstacle inspection.
  • To shift the planning paradigm from space-guided to target-guided exploration using obstacles as natural planning anchors.

Main Methods:

  • ORACLE integrates online target detection using occupied-voxel connected component labelling.
  • A density-aware global coverage planner prioritizes target-dense regions by modulating ATSP costs.
  • A target-guided local planner utilizes Sequential Ordering Problem formulation for direct obstacle observation.

Main Results:

  • ORACLE achieved 98.8% and 99.7% target coverage in dense forest environments, vastly outperforming a space-guided baseline (22.7% and 25.1%).
  • Mission overhead ratio was reduced from over 200% to approximately 127% in tested environments.
  • Ablation studies confirmed zone reactivation is critical for coverage completeness and density weighting enhances path efficiency.

Conclusions:

  • The Object-centric Autonomous Coverage Exploration (ORACLE) framework offers a significant advancement in autonomous inspection tasks.
  • Target-guided exploration, leveraging environmental features, is more effective than traditional space-guided methods for complete and efficient coverage.