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You searched for +publisher:"Delft University of Technology" +contributor:("Hehn, Thomas"). Showing records 1 – 2 of 2 total matches.

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Delft University of Technology

1. van Schouwenburg, Sietse (author). Evaluating SLAM in an urban dynamic environment.

Degree: 2019, Delft University of Technology

Simultaneous Localization And Mapping (SLAM) algorithms provide accurate localization for autonomous vehicles and provide essential information for the path planning module. However, SLAM algorithms as- sume a static environment in order to estimate a location. This assumption influences the pose estimation in dynamic urban environments. The impact of this assumption on day-to-day scenarios of an intelligent vehicle is unknown. A deeper understanding on the effect of dynamic scenarios in an urban environment could lead to simple and robust solutions for SLAM algorithms in intelligent vehicles. The objective of this research is to develop a methodology that isolates the effect of an urban dynamic environment on the per- formance of a SLAM algorithm. This requires constant environment conditions including constant weather conditions, lighting conditions and identical trajectories over time. The methodology is tested with a stereo feature based V-SLAM algorithm called ORB SLAM [19], which illustrates the in-depth analysis that is possi- ble with this experiment. The main research question is: How does a dynamic urban environment influence the pose estimation accuracy of stereo ORB SLAM? Two specific dynamic scenarios are designed to represent a dynamic urban environment: driving behind another vehicle and vehicles approaching on the other side of the road. On these scenarios, an in-depth anal- ysis of ORB SLAM is performed to observe how the algorithm’s design influences the robustness to a dynamic environment. Functions within the algorithm are bypassed to analyze the effect on the performance. Specifi- cally, the place recognition function and map point filtering function are bypassed. The analysis proofs which functions assist in the overall robustness to a dynamic environment. Moreover, an analysis is performed of the algorithm in localization mode to research the effect of utilizing maps that were created under different conditions. The knowledge gained from the full analysis can be utilized to improve other V-SLAM algorithms. The experiment is performed in CARLA [6], an open source simulator. CARLA provides an elaborate sen- sor suite which support multiple camera setups and LIDAR sensors. Furthermore, the simulator provides free maps which represent realistic urban environments and allows for easy and accurate access to the ground truth position. A setup is designed with the simulator that allows complete isolation of the effect of a dy- namic environment. The setup allows full control of lighting conditions, weather conditions and allows iden- tical trajectories over time in different dynamic scenarios. Each scenario is simulated over several different trajectories in which the camera images are converted to rosbags. Each variation of the ORB SLAM algorithm is tested on the produced rosbags. The resulting pose estimations in dynamic conditions are compared to the pose estimations made during static conditions to analyze the effect of dynamic scenarios on the perfor- mance of the algorithm. The method… Advisors/Committee Members: Kooij, Julian (mentor), Hehn, Thomas (mentor), Gavrila, Dariu (graduation committee), Hernandez Corbato, Carlos (graduation committee), Delft University of Technology (degree granting institution).

Subjects/Keywords: SLAM; simulation; computer vision; simulataneous localization and mapping; localization; mapping; visual SLAM; ORB SLAM; CARLA

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

van Schouwenburg, S. (. (2019). Evaluating SLAM in an urban dynamic environment. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:af041e54-7660-4fb1-b68c-0af3aaf27c52

Chicago Manual of Style (16th Edition):

van Schouwenburg, Sietse (author). “Evaluating SLAM in an urban dynamic environment.” 2019. Masters Thesis, Delft University of Technology. Accessed January 15, 2021. http://resolver.tudelft.nl/uuid:af041e54-7660-4fb1-b68c-0af3aaf27c52.

MLA Handbook (7th Edition):

van Schouwenburg, Sietse (author). “Evaluating SLAM in an urban dynamic environment.” 2019. Web. 15 Jan 2021.

Vancouver:

van Schouwenburg S(. Evaluating SLAM in an urban dynamic environment. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 15]. Available from: http://resolver.tudelft.nl/uuid:af041e54-7660-4fb1-b68c-0af3aaf27c52.

Council of Science Editors:

van Schouwenburg S(. Evaluating SLAM in an urban dynamic environment. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:af041e54-7660-4fb1-b68c-0af3aaf27c52


Delft University of Technology

2. van Laar, Patrick (author). Acoustic recognition of motorized vehicles with a moving listener.

Degree: 2019, Delft University of Technology

New measures have to be taken to combat fatalities caused by traffic accidents. Intelligent vehicles have the potential to increase safety, but depend heavily on their automated perception ability. Acoustic perception, an unused sensing modality in this field, has potential for the detection of nearby vehicles, an ability both human drivers and autonomous vehicles could use assistance with. In this thesis two existing datasets, AudioSet a large general purpose dataset and RoadCube a small dedicated vehicle recognition set, are evaluated. Furthermore commonly used acoustic features and classifier algorithm are evaluated. Special attention is given to the influence of a moving listener vehicle on the performance. For the evaluation a new dataset, DriveSound, is captured. It contains samples captured from a listener car, both when its moving or idle. Results show that RoadCube can be used for the detection of road vehicles, but only when the listener is idle. The best performing classifier from RoadCube, a Gaussian Mixture Model classifier surpassed classifiers trained on the evaluation dataset itself with a Matthews Correlation Coefficient (MCC) of 0.34. None of the classifiers performed well on the samples captured by a moving listener, except for the DriveSound-driving classifiers. The Support Vector Machine trained on this dataset attained a MCC of 0.56.

Mechanical Engineering | Vehicle Engineering

Advisors/Committee Members: Hehn, Thomas (mentor), Kooij, Julian (mentor), de Winter, Joost (graduation committee), Jonker, Pieter (graduation committee), Delft University of Technology (degree granting institution).

Subjects/Keywords: Acoustic perception; Intelligent Vehicles; Machine Learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

van Laar, P. (. (2019). Acoustic recognition of motorized vehicles with a moving listener. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:3e74c3a7-9099-48c0-ab26-afb0fa785629

Chicago Manual of Style (16th Edition):

van Laar, Patrick (author). “Acoustic recognition of motorized vehicles with a moving listener.” 2019. Masters Thesis, Delft University of Technology. Accessed January 15, 2021. http://resolver.tudelft.nl/uuid:3e74c3a7-9099-48c0-ab26-afb0fa785629.

MLA Handbook (7th Edition):

van Laar, Patrick (author). “Acoustic recognition of motorized vehicles with a moving listener.” 2019. Web. 15 Jan 2021.

Vancouver:

van Laar P(. Acoustic recognition of motorized vehicles with a moving listener. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 15]. Available from: http://resolver.tudelft.nl/uuid:3e74c3a7-9099-48c0-ab26-afb0fa785629.

Council of Science Editors:

van Laar P(. Acoustic recognition of motorized vehicles with a moving listener. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:3e74c3a7-9099-48c0-ab26-afb0fa785629

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