Tolga Özaslan, PhD

Tolga Özaslan, PhD

Roboticist

Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

MAVNet: An Effective Semantic Segmentation Micro-Network for MAV-based Tasks

Published in IEEE Robotics and Automation Letters (RA-L), 2019

This paper introduces MAVNet, a compact semantic segmentation neural network tailored for micro aerial vehicles (MAVs) operating under size, weight, and power (SWaP) constraints. The network achieves strong performance in real-time aerial perception tasks, including dam and penstock inspection.

Recommended citation: Nguyen, T., Shivakumar, S. S., Miller, I. D., Keller, J., Lee, E. S., Zhou, A., Özaslan, T., Loianno, G., Harwood, J. H., Wozencraft, J. M., Taylor, C. J., & Kumar, V. (2019). "MAVNet: An Effective Semantic Segmentation Micro-Network for MAV-based Tasks." IEEE Robotics and Automation Letters, 4(4), 3908–3915.
Download Paper

Spatio-Temporally Smooth Local Mapping and State Estimation inside Generalized Cylinders with Micro Aerial Vehicles

Published in IEEE Robotics and Automation Letters (RA-L), also presented at ICRA, 2018

This paper presents a state estimation and local mapping system for micro aerial vehicles navigating inside generalized cylindrical tunnels, using a combination of LiDAR and IMU, validated on real-world dam inspections.

Recommended citation: Özaslan, T., Loianno, G., Keller, J., Taylor, C. J., & Kumar, V. (2018). "Spatio-Temporally Smooth Local Mapping and State Estimation inside Generalized Cylinders with Micro Aerial Vehicles." IEEE Robotics and Automation Letters, 3(3), 1755–1762.
Download Paper

The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception

Published in IEEE Robotics and Automation Letters (RA-L), 2018

This paper presents a large-scale dataset captured with a synchronized stereo event camera system across multiple vehicles and platforms, enabling research in stereo depth estimation, SLAM, and visual odometry with event-based vision.

Recommended citation: Zhu, A. Z., Thakur, D., Özaslan, T., Pfrommer, B., Kumar, V., & Daniilidis, K. (2018). "The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception." IEEE Robotics and Automation Letters 2018
Download Paper

Autonomous Navigation and Mapping for Inspection of Penstocks and Tunnels with MAVs

Published in IEEE Robotics and Automation Letters (RA-L), 2017

This work presents an integrated system for the autonomous inspection of penstocks and tunnels using micro aerial vehicles equipped with lidar, IMU, and cameras. The method enables 6-DOF state estimation and real-time mapping inside dark, cylindrical infrastructure.

Recommended citation: Özaslan, T., Loianno, G., Keller, J., Taylor, C. J., Kumar, V., Wozencraft, J. M., & Hood, T. (2017). "Autonomous Navigation and Mapping for Inspection of Penstocks and Tunnels with MAVs." IEEE Robotics and Automation Letters, April 2017.
Download Paper

Conference Papers

Mekanum Platform için Motor Kalibrasyonu

Published in IEEE Sinyal İşleme ve İletişim Uygulamaları Kurultayı (SIU), İstanbul, Türkiye, 2025, 2025

Bu çalışma, Mekanum tekerlekli bir robotik platformda kullanılan motorların kalibrasyon sürecini ele almaktadır. Kalibrasyon, hareket doğruluğu ve yön kontrolünde tutarlılığı artırmak için kritik öneme sahiptir.

Recommended citation: Sarıoğlu, B. B., & Özaslan, T. (2025). "Mekanum Platform için Motor Kalibrasyonu." IEEE Sinyal İşleme ve İletişim Uygulamaları Kurultayı, İstanbul, Türkiye.
Download Paper

Enhancing Trajectory Following in VTOL Cargo UAVs: Adaptive Control in Changing Payload Scenarios

Published in 7th International Symposium on Innovative Approaches in Smart Technologies (ISAS), IEEE, 2023

This paper presents a meta-heuristic approach using Gray Wolf Optimization for adaptive control of VTOL cargo UAVs experiencing changing payload dynamics during flight. PID controller parameters are updated in real-time using a lookup table to maintain stability and accuracy.

Recommended citation: Duru, A. S., Özaslan, T., & Soygüder, S. (2023). "Enhancing Trajectory Following in VTOL Cargo UAVs: Adaptive Control in Changing Payload Scenarios." In Proceedings of the 7th International Symposium on Innovative Approaches in Smart Technologies (ISAS), IEEE, pp. 1–9.
Download Paper

Towards Fully Autonomous Visual Inspection of Dark Featureless Dam Penstocks Using MAVs

Published in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016

This paper presents a visual-inertial navigation and mapping framework for autonomous inspection of dark and featureless dam penstocks using a custom hex-rotor MAV equipped with cameras and lidars.

Recommended citation: Özaslan, T., Mohta, K., Keller, J., Mulgaonkar, Y., Taylor, C. J., Kumar, V., Wozencraft, J. M., & Hood, T. (2016). "Towards Fully Autonomous Visual Inspection of Dark Featureless Dam Penstocks Using MAVs." In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4998–5005.
Download Paper

Inspection of Penstocks and Featureless Tunnel-like Environments Using Micro UAVs

Published in Field and Service Robotics (FSR) : Results of the 9th International Conference, Springer, 2015

This paper presents a Rao–Blackwellized particle filter–based method for autonomous UAV inspection in GPS-denied, featureless tunnel-like environments such as penstocks, validated with real-world experiments.

Recommended citation: Özaslan, T., Shen, S., Mulgaonkar, Y., Michael, N., & Kumar, V. (2015). "Inspection of Penstocks and Featureless Tunnel-like Environments Using Micro UAVs." In Field and Service Robotics: Results of the 9th International Conference, pp. 123–136, Springer.
Download Paper

Books / Book Chapters

Inertial Measurement Units: Modeling and Calibration

Published in Eğitim Yayınevi, 2025

This work provides a comprehensive overview of inertial sensors used in robotics, detailing the structure, calibration, and mathematical modeling of accelerometers, gyroscopes, and magnetometers for state estimation.

Recommended citation: Özaslan, T. (2025). "Inertial Measurement Units: Modeling and Calibration." Eğitim Yayınevi.
Download Paper

Patents

Identifying Background Features Using LiDAR

Published in U.S. Patent Application Publication US 2021/0373173 A1, 2021

A patented method for identifying background features in LiDAR data using spherical modeling and graph-based segmentation.

Recommended citation: Özaslan, T. (2021). "Identifying Background Features Using LiDAR." U.S. Patent Application Publication US 2021/0373173 A1.
Download Paper

Thesis