Browsing by Author "Tiirats, Timo"

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    Mehitamata langevarjule arendusplatvormi loomine
    (Tartu Ülikool, 2020) Tiirats, Timo; Abels, Artur; Moks, Andres; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Tehnoloogiainstituut
    Maailmas kasvab järjest trend saata tellitud pakke laiali mehitamata sõidukeid kasutades. Sellest lähtuvalt on käesoleva töö eesmärk valmistada arendusplatvorm mehitamata langevarju juhtimiseks. Töö käigus arendatakse välja sobiv elektroonikalahendus koos sobivate andurite ning aktuaatoritega. Lisaks valmistatakse seadmele langevarju kinnitamiseks korpus. Valminud seadme puhul on pööratud tähelepanu kasutajamugavusele ning võimalusele kasutada seda tulevikus erinevate lendamisalgoritmide väljatöötamisel. Töö käigus kirjutatud püsivara põhjal viidi läbi katsed, et testida seadmesse paigaldatud andurite ja mootorite tööd. In english: There is a growing trend in the world to distribute ordered packages using unmanned vehicles. For this reason, the aim of this thesis is to create a development platform for controlling an unmanned parachute. In the course of the research, a suitable electronics solution is developed together with suitable sensors and actuators. In addition, a housing is made for attaching the parachute to the device. In the case of the completed device, attention has been paid to user comfort and the possibility to use it in the future in the development of various flight algorithms. Based on the firmware written during the work, experiments were performed to test the operation of the sensors and motors installed in the device.
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    Object Recognition Using a Sparse 3D Camera Point Cloud
    (Tartu Ülikool, 2023) Tiirats, Timo; Matiisen, Tambet, juhendaja; Bogdanov, Jan, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
    The demand for higher precision and speed of computer vision models is increasing in autonomous driving, robotics, smart city and numerous other applications. In that context, machine learning is gaining increasing attention as it enables a more comprehensive understanding of the environment. More reliable and accurate imaging sensors are needed to maximise the performance of machine learning models. One example of a new sensor is LightCode Photonics’ 3D camera. The thesis presents a study to evaluate the performance of machine learning-based object recognition in an urban environment using a relatively low spatial resolution 3D camera. As the angular resolution of the camera is smaller than in commonly used 3D imaging sensors, using the camera output with already published object recognition models makes the thesis unique and valuable for the company, providing feedback for LightCode Photonics’ current camera specifications for machine learning tasks. Furthermore, the knowledge and materials could be used to develop the company’s object recognition pipeline. During the thesis, a new dataset is generated in CARLA Simulator and annotated, representing the 3D camera in a smart city application. Changes to CARLA Simulator source code were implemented to represent the actual camera closely. The thesis is finished with experiments where PointNet semantic segmentation and PointPillars object detection models are applied to the generated dataset. The generated dataset contained 4599 frames, of which 2816 were decided to use in this thesis. PointNet model applied to the dataset could predict the semantically segmented scene with similar accuracy as in the original paper. A mean accuracy of 44.15% was achieved with PointNet model. On the other hand, PointPillars model was unable to perform on the new dataset.