What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Computer network
- Computer vision
Her primary scientific interests are in Artificial intelligence, Computer vision, Wireless sensor network, Visual odometry and Key.
Artificial intelligence is frequently linked to Pattern recognition in her study.
Her biological study spans a wide range of topics, including Robot, Odometry, Robustness and Hidden Markov model.
Her Wireless sensor network study incorporates themes from Routing, SQL, Database, Variety and Software.
Her Visual odometry research includes themes of Motion estimation and Deep learning.
Her Key study deals with Domain intersecting with Sustainability.
Her most cited work include:
- DeepVO: Towards end-to-end visual odometry with deep Recurrent Convolutional Neural Networks (298 citations)
- Supporting Search and Rescue Operations with UAVs (269 citations)
- Delay-bounded routing in vehicular ad-hoc networks (143 citations)
What are the main themes of her work throughout her whole career to date?
Niki Trigoni spends much of her time researching Artificial intelligence, Computer vision, Wireless sensor network, Real-time computing and Robot.
Odometry, Deep learning, Robustness, Visual odometry and Point cloud are among the areas of Artificial intelligence where the researcher is concentrating her efforts.
She combines subjects such as Radar and Key with her study of Computer vision.
Her research in Wireless sensor network tackles topics such as Distributed computing which are related to areas like Query optimization.
She interconnects Tracking, Positioning system, Simulation, Non-line-of-sight propagation and Global Positioning System in the investigation of issues within Real-time computing.
Her Robot research incorporates elements of Augmented reality, Control theory, Task and Reinforcement learning.
She most often published in these fields:
- Artificial intelligence (43.39%)
- Computer vision (28.51%)
- Wireless sensor network (20.25%)
What were the highlights of her more recent work (between 2018-2021)?
- Artificial intelligence (43.39%)
- Computer vision (28.51%)
- Deep learning (9.09%)
In recent papers she was focusing on the following fields of study:
Her primary areas of study are Artificial intelligence, Computer vision, Deep learning, Radar and Odometry.
Her study on Artificial intelligence is mostly dedicated to connecting different topics, such as Task.
Her Computer vision research is multidisciplinary, incorporating perspectives in Artificial neural network, Lidar and Detector.
Niki Trigoni has researched Deep learning in several fields, including Depth map, Simultaneous localization and mapping and Unsupervised learning, Pattern recognition.
Her Radar research is multidisciplinary, relying on both Extremely high frequency, Robot and Real-time computing.
She works mostly in the field of Odometry, limiting it down to concerns involving Trajectory and, occasionally, Augmented reality and Spatial contextual awareness.
Between 2018 and 2021, her most popular works were:
- RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds (126 citations)
- Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds (69 citations)
- GANVO: Unsupervised Deep Monocular Visual Odometry and Depth Estimation with Generative Adversarial Networks (58 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer network
- Computer vision
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Deep learning, Odometry and Visual odometry.
Her Artificial intelligence study combines topics in areas such as Key and Pattern recognition.
The Computer vision study combines topics in areas such as Radar and Representation.
The study incorporates disciplines such as Depth map, Simultaneous localization and mapping, Pose and Robustness in addition to Deep learning.
Her work deals with themes such as Artificial neural network, Inertial measurement unit and Sensor fusion, which intersect with Odometry.
Her studies in Visual odometry integrate themes in fields like Variable, Monocular, Knowledge transfer, Knowledge engineering and Feature extraction.
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