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Related Experiment Video

Updated: Jul 1, 2026

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats
06:52

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats

Published on: April 3, 2026

Dataset and tools for benchmarking multi-sensor multi-people tracking for ground robots.

Roberto Larcher1, Davide Farina1, Marco Piazzola1

  • 1Spindox Labs, Via alla cascata 56/c, 38123 Trento, Italy.

Data in Brief
|June 30, 2026
PubMed
Summary
This summary is machine-generated.

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This study introduces a new dataset and tools for evaluating Robot Operating System 2 (ROS 2) people tracking on ground robots. It enables accurate tracking in complex, dynamic environments using multiple sensors.

Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Accurate people tracking is crucial for socially compliant human-robot interaction.
  • Existing benchmarks for Robot Operating System 2 (ROS 2) people tracking lack diversity and comprehensive evaluation metrics.

Purpose of the Study:

  • To introduce a novel dataset and software tools for benchmarking ROS 2 people tracking modules.
  • To facilitate the evaluation of ground robot perception systems in dynamic human environments.

Main Methods:

  • Collected diverse sequences of ground robot navigation among people using RGBD cameras and LiDAR.
  • Developed automated software tools for computing tracking-relevant metrics on the dataset.
  • Designed dataset variations including people density, behaviors, obstacles, and robot movements.
Keywords:
Ground robotLiDARPeople trackingRGBDROS 2Socially compliant navigation

Related Experiment Videos

Last Updated: Jul 1, 2026

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats
06:52

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats

Published on: April 3, 2026

Main Results:

  • A comprehensive dataset for evaluating people tracking algorithms in realistic scenarios.
  • Software tools enabling automated performance assessment of ROS 2 tracking modules.
  • Identification of scenarios where tracking performance degrades.

Conclusions:

  • The novel dataset and tools provide a standardized benchmark for ROS 2 people tracking.
  • This resource will advance the development of more robust and reliable robot perception systems.
  • Facilitates the assessment of robot tracking capabilities in complex, human-populated areas.