Predictive Mover Detection and Tracking in Cluttered Environments

Abstract

This paper describes the design and experimental evaluation of a system that enables a vehicle to detect and track moving objects in real-time. The approach investigated in this work detects objects in LADAR scan lines and tracks these objects (people or vehicles) over time. The system can fuse data from multiple scanners for 360 deg. coverage. The resulting tracks are then used to predict the most likely future trajectories of the detected objects. The predictions are intended to be used by a planner for dynamic object avoidance. The perceptual capabilities of our system form the basis for safe and robust navigation in robotic vehicles, necessary to safeguard soldiers and civilians operating in the vicinity of the robot.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 2006
Accession Number
ADA481463

Entities

People

  • Christoph Mertz
  • Luis Navarro-serment
  • Martial Hebert

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy
  • Sensors

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Collision Avoidance Systems
  • Computer Vision
  • Detection
  • Detectors
  • Filters
  • Kalman Filters
  • Measurement
  • Navigation
  • Probability
  • Simultaneous Localization And Mapping
  • Test And Evaluation
  • Trajectories
  • Unmanned Vehicles
  • Vehicles
  • Warning Systems

Fields of Study

  • Engineering

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Radar Systems Engineering.
  • Unmanned Aerial System (UAS) Autonomous Capabilities and Mission Reconnaissance.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • Autonomy