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Dimitrios Tzovaras

8 accepted papers

2024

Robot Active Vision-Based Path Planning for Localization Improvement in Indoor Environments

Sotirios Barlakas, Dimitrios Alexiou, Kosmas Tsiakas, Dimitrios Katsatos, Ioannis Kostavelis, Dimitrios Giakoumis +2
IROS 2024poster

Reliable and robust navigation of autonomous mobile robots in indoor environments faces significant challenges due to the absence of GPS, visual degradation, repetitive structures, illumination variations, and low texture. These factors adversely affect localization systems. Current robots often use…

Cited by 1Source ↗Scholar ↗
2023

Leveraging Multimodal Sensing and Topometric Mapping for Human-Like Autonomous Navigation in Complex Environments

Kosmas Tsiakas, Dimitrios Alexiou, Dimitrios Giakoumis, Antonios Gasteratos, Dimitrios Tzovaras
IROS 2023poster

Autonomous vehicle navigation in complex and unpredictable outdoor environments requires extensive and detailed understanding of the surrounding area and compliance with the traffic rules. In this paper, we attempt to imitate human driver behavior towards autonomous navigation that is suitable for d…

Cited by 4Source ↗Scholar ↗
2022

Loop Closure Detection and SLAM in Vineyards with Deep Semantic Cues

Alexios Papadimitriou, Ioannis Kleitsiotis, Ioannis Kostavelis, Ioannis Mariolis, Dimitrios Giakoumis, Spiriden Likothanassis +1
ICRA 2022poster

Automation of vineyards cultivation necessitates for mobile robots to retain accurate localization system. The paper introduces a stereo vision-based Graph-Simultaneous Localization and Mapping (Graph-SLAM) pipeline custom-tailored to the specificities of vineyard fields. Graph-SLAM is reinforced wi…

Cited by 25Source ↗Scholar ↗
2021

Autonomous Vehicle Navigation in Semi-structured Environments Based on Sparse Waypoints and LiDAR Road-tracking

Kosmas Tsiakas, Ioannis Kostavelis, Antonios Gasteratos, Dimitrios Tzovaras
IROS 2021poster

During the last decades, the research endeavours on autonomous driving found great resonance in Advanced Driver-Assistance Solutions that equipped the contemporary civilian vehicles and significantly boosted their driver-less mobility. The existing applications are mostly focused on urban scenarios…

Cited by 16Source ↗Scholar ↗
2021

Pallet detection and docking strategy for autonomous pallet truck AGV operation

Efthimios Tsiogas, Ioannis Kleitsiotis, Ioannis Kostavelis, Andreas Kargakos, Dimitris Giakoumis, Marc Bosch-Jorge +4
IROS 2021poster

Automated guided vehicles operation in human populated factory environments is a challenging task, especially when there is a demand to operate without following fixed paths defined by guide wires, magnetic tape, magnets, or transponders embedded in the floor. The paper at hand introduces a vision-b…

Cited by 26Source ↗Scholar ↗
2018

3D Underground Mapping with a Mobile Robot and a GPR Antenna

Georgios Kouros, Ioannis Kotavelis, Evangelos Skartados, Dimitrios Giakoumis, Dimitrios Tzovaras, Alessandro Simi +1
IROS 2018poster

Automatic subsurface mapping is essential in the construction services, as it is anticipated to become the main operational environment of the future robots to be realized in the respective domain. Towards this direction, the paper at hand, introduces for the first time herein, an integrated framewo…

Cited by 37Source ↗Scholar ↗
2018

RAMCIP - A Service Robot for MCI Patients at Home

Georgia Peleka, Andreas Kargakos, Evangelos Skartados, Ioannis Kostavelis, Dimitrios Giakoumis, Iason Sarantopoulos +11
IROS 2018poster

This video features RAMCIP, a new service robot developed to provide proactive and discreet assistance to elderly with Mild Cognitive Impairments (MCI), supporting their daily activities at home. Starting with a thorough analysis of needs and requirements of the target population, the RAMCIP robot w…

Cited by 22Source ↗Scholar ↗
2017

Key-frame Extraction With Semantic Graphs in Assembly Processes

Grigorios S. Piperagkas, Ioannis Mariolis, Dimosthenis Ioannidis, Dimitrios Tzovaras
RA-L 2017

In this letter, we present a novel method for key-frame extraction in assembly processes, with the support of visual sensors. The method is based on the construction of a sequence of weighted graphs containing the semantics of the underlying scene, encoded as primitive temporal and spatial relations

Cited by 5Source ↗Scholar ↗