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Benjamin Ramtoula

PDRA - MRG

Benjamin is a Postdoctoral Research Assistant within the Mobile Robotics Group (MRG), where he focuses on making robots that can be robustly deployed in diverse, novel environments.

He originally joined the Oxford Robotics Institute in 2020 as a DPhil student at Wolfson College, supervised by Prof. Paul Newman as part of the Autonomous Intelligent Machines and Systems (AIMS) CDT. His doctoral research explored how general representations learned by vision foundation models could be used to represent and compare image datasets across domains and tasks. He applied these solutions to downstream robotics tasks, such as place recognition and selecting relevant maps for robot localisation.

Prior to his DPhil, Benjamin earned a BSc and MSc in Microengineering from EPFL, where he worked on human motion modelling under Prof. Aude Billard. He also holds an MSc in Aerospace Engineering from Polytechnique Montréal, where he researched multi-robot SLAM and object pose estimation under Prof. Giovanni Beltrame.

During his studies, Benjamin was a visiting student in Prof. Mac Schwager's group at Stanford University and interned with NASA JPL’s CoSTAR team. There, he applied his work on object pose estimation to localise objects in underground environments for the DARPA Subterranean Challenge.

Most Recent Publications

3D foundation model-based loop closing for decentralized Collaborative SLAM

3D foundation model-based loop closing for decentralized Collaborative SLAM

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VDNA-PR: using general dataset representations for robust sequential visual place recognition

VDNA-PR: using general dataset representations for robust sequential visual place recognition

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What you see is what you get: experience ranking with deep neural dataset-to-dataset similarity for topological localisation

What you see is what you get: experience ranking with deep neural dataset-to-dataset similarity for topological localisation

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Visual DNA: representing and comparing images using distributions of neuron activations

Visual DNA: representing and comparing images using distributions of neuron activations

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Towards Collaborative Simultaneous Localization and Mapping: A Survey of the Current Research Landscape

Towards Collaborative Simultaneous Localization and Mapping: A Survey of the Current Research Landscape

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