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31	Automated Performance Test Generation and Comparison for Complex Data Structures - Exemplified on High-Dimensional Spatio-Temporal Indices Menninghaus, Mathias 23 August 2018 (has links) There exist numerous approaches to index either spatio-temporal or high-dimensional data. None of them is able to efficiently index hybrid data types, thus spatio-temporal and high-dimensional data. As the best high-dimensional indexing techniques are only able to index point-data and not now-relative data and the best spatio-temporal indexing techniques suffer from the curse of dimensionality, this thesis introduces the Spatio-Temporal Pyramid Adapter (STPA). The STPA maps spatio-temporal data on points, now-values on the median of the data set and indexes them with the pyramid technique. For high-dimensional and spatio-temporal index structures no generally accepted benchmark exists. Most index structures are only evaluated by custom benchmarks and compared to a tiny set of competitors. Benchmarks may be biased as a structure may be created to perform well in a certain benchmark or a benchmark does not cover a certain speciality of the investigated structures. In this thesis, the Interface Based Performance Comparison (IBPC) technique is introduced. It automatically generates test sets with a high code coverage on the system under test (SUT) on the basis of all functions defined by a certain interface which all competitors support. Every test set is performed on every SUT and the performance results are weighted by the achieved coverage and summed up. These weighted performance results are then used to compare the structures. An implementation of the IBPC, the Performance Test Automation Framework (PTAF) is compared to a classic custom benchmark, a workload generator whose parameters are optimized by a genetic algorithm and a specific PTAF alternative which incorporates the specific behavior of the systems under test. This is done for a set of two high-dimensional spatio-temporal indices and twelve variants of the R-tree. The evaluation indicates that PTAF performs at least as good as the other approaches in terms of minimal test cases with a maximized coverage. Several case studies on PTAF demonstrate its widespread abilities. performance tests test case generation benchmark generation workload generation indexing access methods high-dimensional spatio-temporal subway-track planning performance comparison algorithm engineering performance measurement java software engineering 54.62 - Datenstrukturen 54.52 - Software engineering D.2.5 - Testing and Debugging D.2.8 - Metrics E.1 - DATA STRUCTURES D.2.2 - Design Tools and Techniques ddc:004
32	Asynchronous Event-Feature Detection and Tracking for SLAM Initialization Ta, Tai January 2024 (has links) Traditional cameras are most commonly used in visual SLAM to provide visual information about the scene and positional information about the camera motion. However, in the presence of varying illumination and rapid camera movement, the visual quality captured by traditional cameras diminishes. This limits the applicability of visual SLAM in challenging environments such as search and rescue situations. The emerging event camera has been shown to overcome the limitations of the traditional camera with the event camera's superior temporal resolution and wider dynamic range, opening up new areas of applications and research for event-based SLAM. In this thesis, several asynchronous feature detectors and trackers will be used to initialize SLAM using event camera data. To assess the pose estimation accuracy between the different feature detectors and trackers, the initialization performance was evaluated from datasets captured from various environments. Furthermore, two different methods to align corner-events were evaluated on the datasets to assess the difference. Results show that besides some slight variation in the number of accepted initializations, the alignment methods show no overall difference in any metric. Overall highest performance among the event-based trackers for initialization is HASTE with mostly high pose accuracy and a high number of accepted initializations. However, the performance degrades in featureless scenes. CET on the other hand shows mostly lower performance compared to HASTE. Visual SLAM SLAM Traditional cameras event camera events neuromorphic camera silicon retina dynamic vision sensor Camera motion Illumination variation Rapid camera movement Visual quality degradation Challenging environments Search and rescue Event cameras Temporal resolution Dynamic range Event-based SLAM Asynchronous feature detectors Trackers Pose estimation accuracy Initialization performance Datasets Corner-event alignment HASTE tracker Pose accuracy Featureless scenes CET tracker Initialization acceptance Performance comparison Signal Processing Signalbehandling Robotics Robotteknik och automation Control Engineering Reglerteknik

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Automated Performance Test Generation and Comparison for Complex Data Structures - Exemplified on High-Dimensional Spatio-Temporal Indices

Asynchronous Event-Feature Detection and Tracking for SLAM Initialization