Cristiano Premebida

About

Cristiano Premebida is a leading researcher in intelligent robotic perception systems, with a focus on multimodal sensor fusion, human-robot interaction, and semantic scene understanding. His work bridges computer vision, machine learning, and robotics, developing probabilistic and deep-learning approaches for object recognition, activity recognition, and place classification. Premebida’s most cited paper (85 citations) introduces a probabilistic framework for recognizing human everyday activities from RGB-D body motion data, advancing assistive robotics. He has also pioneered multimodal deep-learning strategies for combining camera and LIDAR data in autonomous vehicles (46 citations) and developed affective facial expression recognition systems for human-robot interaction (45 citations). His research on dynamic Bayesian networks for semantic place classification (37 citations) and probabilistic models for daily activity recognition (36 citations) has shaped robot-assisted living technologies. With over 300 total citations across his top works, Premebida’s contributions are foundational to creating perceptually aware robots capable of navigating, understanding, and interacting with human environments. His work on simultaneous segmentation and superquadrics fitting in laser-range data (28 citations) further demonstrates his impact on 3D perception for mobile robotics.

Research Focus

Key Achievements

Top Papers

1. 1
   A probabilistic approach for human everyday activities recognition using body motion from RGB-D images

   85 citations · 2014
2. 2
   Intelligent Robotic Perception Systems

   46 citations · 2019
3. 3
   Multimodal Deep-Learning for Object Recognition Combining Camera and LIDAR Data

   46 citations · 2020
4. 4
   Affective facial expressions recognition for human-robot interaction

   45 citations · 2017
5. 5
   Dynamic Bayesian network for semantic place classification in mobile robotics

   37 citations · 2016
6. 6
   Probabilistic human daily activity recognition towards robot-assisted living

   36 citations · 2015
7. 7
   Simultaneous Segmentation and Superquadrics Fitting in Laser-Range Data

   28 citations · 2014
8. 8
   Applying probabilistic Mixture Models to semantic place classification in mobile robotics

   20 citations · 2015
9. 9
   AttDLNet: Attention-Based Deep Network for 3D LiDAR Place Recognition

   18 citations · 2022
10. 10
    3D point cloud downsampling for 2D indoor scene modelling in mobile robotics

    17 citations · 2017

Key Collaborators

Contact & Links