Print Email Facebook Twitter Pedestrian Detection and Tracking for Mobile Robots in Human Environments Title Pedestrian Detection and Tracking for Mobile Robots in Human Environments Author Marcelis, N.H.H. (TU Delft Mechanical, Maritime and Materials Engineering) Contributor Ferreira de Brito, B.F. (mentor) Alonso Mora, J. (graduation committee) Degree granting institution Delft University of Technology Programme Mechanical Engineering | Biomechanical Design - BioRobotics Date 2021-12-15 Abstract With the performance of current motion planning methods being highly dependent on the quality of the perception system, robust 3D multi-object detection and tracking are vital for autonomous driving applications. Despite all the advancements in 2D and 3D object detectors, robust tracking of pedestrians in dense scenarios is still a challenging subject for small Automated Guided Vehicles (AGVs). Most research in the field of object detection and tracking focuses on autonomous cars, neglecting the design challenges that come with small AGVs.This thesis presents a real-time multi-modal multi-pedestrian detection and tracking pipeline for small mobile robots. The framework integrates five RGB-D cameras and a LiDAR sensor to achieve real-time pedestrian detection and tracking. The system relies on state-of-the-art 2D and 3D object detectors, a sensor fusion and filtering scheme, and a 3D object tracker. Moreover, to improve detection and tracking performance, we have collected a pedestrian dataset tailored for small AGVs. We use this dataset to train the 3D pedestrian detector and evaluate the performance of the pedestrian detectors and tracker. Evaluation of the proposed framework demonstrated the ability to robustly detect and track multiple pedestrians up to a distance of 10 meters. We open-sourced our framework at: https://github.com/bbrito/amr_navigation. Subject Machine LearningDeep LearningObject DetectionObject TrackingComputer VisionAutonomous VehiclesIntelligent Vehicles To reference this document use: http://resolver.tudelft.nl/uuid:6a780003-cb20-4a8c-ac90-2867274a65d6 Part of collection Student theses Document type master thesis Rights © 2021 N.H.H. Marcelis Files PDF Thesis_Report_Niels_Marcelis.pdf 14 MB Close viewer /islandora/object/uuid:6a780003-cb20-4a8c-ac90-2867274a65d6/datastream/OBJ/view