Papers
17
Total Citations
191
H-Index
7
About
Pedro Miraldo is a researcher whose work spans computer vision, robotics, and autonomous systems, with particular expertise in camera geometry, pose estimation, and human-aware robot navigation. His foundational contributions to generalized camera models have advanced the field of pose estimation, including his direct solutions to minimal generalized pose problems and planar pose estimation using 3D line features — work that underpins modern augmented reality and robotic localization pipelines. His 2021 paper on incremental structure from motion using lines further extended these geometric foundations, recognizing that line-based representations offer richer environmental information than traditional point-based approaches. Beyond geometric vision, Miraldo has made meaningful contributions to mobile robotics, developing real-time deep learning pedestrian detectors for human-aware navigation — a body of work accumulating over 30 citations across multiple publications — and probabilistic frameworks for efficient object search in domestic environments. His involvement in the influential RoCKIn project, his most-cited work with 71 citations, demonstrates a commitment to rigorous experimental benchmarking in cognitive robotics, helping establish reproducible evaluation standards across European robotics competitions. Together, his research reflects a researcher who bridges theoretical rigor with practical autonomous systems challenges, shaping how robots perceive, localize, and safely navigate human environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3On Incremental Structure from Motion Using Lines18 citations · 2021
- 4
- 5Planar pose estimation for general cameras using known 3D lines12 citations · 2014
- 6Direct Solution to the Minimal Generalized Pose10 citations · 2014
- 7
- 8
- 9A real-time Deep Learning pedestrian detector for robot navigation5 citations · 2017
- 10RoCKIn - Benchmarking Through Robot Competitions5 citations · 2017