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Research interests

I have been working on sensor fusion for autonomous driving purposes. By integrating several sensors, we overcome the shortages of individual sensors and improve the reliability of the system. I have applied Bayesian statistical framework to integrate various sensors to achieve different tasks. I have integrated the laser scanner and GNSS/IMU to track the moving objects adjacent to the autonomous vehicle.

By the advancement of machine learning, deep learning approaches are applied to more effectively integrate sensors and achieve higher accuracy. My students and I apply deep learning to integrate thermal and visual images for human detection. In addition, I have been developing an approach to simultaneously integrate and model the sensors connecting Bayesian and deep learning based sensor fusion.

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Units of measurement Engineering & Materials Science
Navigation Engineering & Materials Science
Sensors Engineering & Materials Science
Kalman filters Engineering & Materials Science
Lasers Engineering & Materials Science
Pipelines Engineering & Materials Science
Global positioning system Engineering & Materials Science
Luminance Engineering & Materials Science

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Research Output 2014 2018

  • 34 Citations
  • 4 h-Index
  • 5 Chapter
  • 5 Article
  • 3 Conference contribution

An Evaluation Pipeline For Indoor Laser Scanning Point Clouds

Karam, S., Peter, M., Hosseinyalamdary, S. & Vosselman, G., 26 Sep 2018, ISPRS TC I Mid-term Symposium Innovative Sensing – From Sensors to Methods and Applications (Volume IV-1) 10–12 October 2018, Karlsruhe, Germany. Jutzi, B., Weinmann, M. & Hinz, S. (eds.). Karlsruhe: International Society for Photogrammetry and Remote Sensing (ISPRS), Vol. IV-1. p. 85-92 (ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences).

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

Open Access
Interiors (building)
Data acquisition
4 Citations (Scopus)

Deep Kalman Filter: Simultaneous Multi-Sensor Integration and Modelling; A GNSS/IMU Case Study

Hosseinyalamdary, S., 2018, In : Sensors (Switserland). 18, 5, 15 p., 1316.

Research output: Contribution to journalArticleAcademicpeer-review

Open Access
satellite navigation systems
Units of measurement
Kalman filters
Research Design

Error Modeling of Reduced IMU using Recurrent Neural Network

Hosseinyalamdary, S. & Balazadegan Sarvrood, Y., 2017, Proceedings of the 8th international conference on Indoor Positioning and Indoor Navigation, 18-21 September 2017, Sapporo, Japan. 4 p.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Open Access
1 Citation (Scopus)

Lane level localization : using images and HD maps to mitgate the lateral error

Hosseinyalamdary, S. & Peter, M., 2017, Proceedings of ISPRS Hannover Workshop : HRIGI 17 – CMRT 17 – ISA 17 – EuroCOW 17, 6–9 June 2017, Hannover, Germany. Heipke, C. (ed.). Hannover: International Society for Photogrammetry and Remote Sensing (ISPRS), Vol. XLII-1/W1. p. 129-134 (ISPRS Archives).

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Open Access
2 Citations (Scopus)

A Bayesian approach to traffic light detection and mapping

Hosseinyalamdary, S. & Yilmaz, A., 2016, In : ISPRS journal of photogrammetry and remote sensing. 125, p. 184-192

Research output: Contribution to journalArticleAcademicpeer-review


Merging Images, Trajectory, and Point Clouds for 3D Object Tracking

Siavash Hosseinyalamdary (Recipient), A. Yilmaz (Recipient) & Polun (Ryan) Polun (Ryan) Lai (Recipient), 2014