Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 67 for “"data association"”.
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Open-Set Object Based Data Association
… YCB objects, are used to provide input to the data association problem, which limits the scope of the system to environments that it has been trained on. With advancements in foundational models, we can extend this representation for objects that are not known a priori and do not have a labeled …
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Data Association Algorithms and Representations for Robust Geometric Perception
Data association is a fundamental requirement of geometric estimation in robotics. Identifying correspondences between measurements and models enables estimation processes to incorporate more data, in general leading to better estimates. However, sensor data is replete with noise and spurious …
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A comparison of data association techniques for Simultaneous Localization and Mapping
… property is the ability to deal with unknown data association and its robustness to data association errors. The problem of data association has also received a great deal of attention in the robotics literature in recent years, and various solutions have been proposed. In an effort to both …
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Ballistic missile tracking using the interacting multiple model joint probabilistic data association filter
… interacting multiple model joint probabilistic data association filter to effectively track a ballistic missile and detect decoys and maneuvers is the focus of this thesis. Model development and data association schemes are discussed along with optimized values for selected parameters. …
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Robust non-Gaussian semantic simultaneous localization and mapping
… most prominent case in which this arises is from data association uncertainty, where we do not know with certainty what objects in the environment caused the measurement made by our sensor. The semantic class of an object can help to inform data association; a detection classified as a door is …
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Approaches to Multiple-source Localization and Signal Classification
… environments, two solutions to the complex data association problem are addressed. The first proposed technique offers a less complex solution to the data association problem than a brute-force approach at the expense of some degradation in performance. For the second technique, the process …
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Active Simultaneous Localization and Mapping in Perceptually Aliased Underwater Environments
… that makes it difficult to integrate its data into SLAM frameworks. Furthermore, the lack of training data makes it difficult to accurately obtain object detections from sonar for semantic, or object-based, SLAM. Finally, the technique of actively choosing trajectories that can take into …
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Robust non-Gaussian semantic simultaneous localization and mapping
… most prominent case in which this arises is from data association uncertainty, where we do not know with certainty what objects in the environment caused the measurement made by our sensor. The semantic class of an object can help to inform data association; a detection classified as a door is …
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Modern Bayesian Object Tracking: Challenges and Solutions
… problem is typically divided into two sub-tasks: data association and state estimation. Data association involves identifying which measurements correspond to which object, while state estimation involves estimating the position, velocity, and other properties of each object. The major challenge …
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Uncertainty Quantification of Tightly Integrated LiDAR/IMU Localization Algorithms
… exceeds predefined acceptability limits. IMU data improves LiDAR position and orientation (pose) prediction and LiDAR limits the IMU error drift over time. In addition, LiDAR return-light intensity measurements improve landmarks recognition. As compared to using the sensors individually, …
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Performance analysis and algorithm enhancement of feature-aided-tracker (FAT) simulation software using 1-D high-range-resolution (HRR) radar signature profiles
… profiles were used to assist in track-to-report data association through classification-aided and signature-aided tracking (CAT and SAT) algorithms. Profiles were obtained from the DARPA-sponsored Moving Target Feature Phenomenology (MTFP) program. Performance Analysis of this simulation tool …
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Multiple-Target Tracking in Complex Scenarios
… tracking of multiple targets. The multi-modal data consists of the measurements collected from a radar, an</p><p>infrared camera and a human scout. We also propose a unified framework for multi-modal sensor management</p><p>that comprises sensor selection: SS), resource allocation: RA) and data …
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Underwater Semantic Simultaneous Localization and Mapping
… due to the lack of large labeled underwater datasets and the challenging sensor modalities specific to underwater environments. To address these shortcomings, this thesis develops a range of methodologies to advance underwater semantic SLAM capabilities. First, self-supervised learning and …
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Inferring the Existence of Geometric Primitives to Represent Non-Discriminable Data
… in general primitive fitting, non-discriminable data presents additional data association challenges. We propose to address these challenges by estimating the existence rather than parameters of geometric primitives, and explore various options to do so. We first explore a sampling-based …
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Multi-modal and inertial sensor solutions for navigation-type factor graphs
… the frontend to defer ambiguities (such as data association) until inference. Probabilistic consensus ensures dominant modes across all measurement information. Our approach propagates continuous beliefs on the Bayes (Junction) tree, which is an efficient symbolic refactorization of the …
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Multiarray Passive Acoustic Localization and Tracking
Wireless sensor networks and data fusion has received increasing attention in recent years, due to the ever increasing computational power, battery and wireless technology, and proliferation of sensor modalities. Notably, the application of acoustic sensors and arrays of sensors has expanded to …
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NEURO-SYMBOLIC AI APPROACHES FOR SENSOR-BASED HUMAN ACTIVITY RECOGNITION
… opacity and the scarcity of labeled training data. Fortunately, common sense and domain knowledge about activity execution can improve purely data-driven approaches. Indeed, in the general machine learning community, Neuro-Symbolic AI (NeSy) methods are emerging to combine DL models with more …
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Applied Probabilistic Inference: Model Estimation For Hvac Predictive Controls And All-Weather Perception For Autonomous Vehicles
… By leveraging building topology and measurement data, the filter quickly learns parameters of a thermal network during periods of known or constrained loads and then characterizes unknown loads in order to provide accurate 24+ hour energy predictions. Performance was validated with EnergyPlus …
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Data Analysis for Emotion Identification in Text
… identify emotional sentences in an article using data analysis technologies. Two types of methods are proposed to solve the problem. A straightforward method of identifying emotional sentences is to formulate it as a classification problem. A classifier based on Linear Discriminant Analysis (LDA) …
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Monocular vision based navigation using image moments of polygonal features
… reduce the computational complexity of data association and produce a map that is geometrically and structurally more meaningful. Each region can be characterized using its depth and orientation with respect to the camera and an polygon detection and tracking algorithm is developed. The …
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