About me

Hey, I’m Alex. I work at Nvidia on real‑time 3D reconstruction for robotics using embedded GPUs.

I finished my Ph.D. at the Autonomous Systems Lab at ETH Zürich, in Switzerland in 2021. My research focused on 3D map-building for rotary-wing UAVs, and more generally on mapping large-scale environments on computationally constrained platforms. I spent the final part of my Ph.D. as a visiting scientist in the Microsoft Mixed Reality & AI Zurich Lab.

Prior to my Ph.D., I wrote state estimation and machine learning algorithms for a Formula-1 team, and spent time prior to that, developing acoustic devices which I helped install in heavy machinery. I finished a Masters’s in Robotics, Systems, and Control in 2015, also at ETH, and I hold a Bachelor’s degree from Canterbury University, located in Christchurch, New Zealand, where I grew up.

I love working with passionate people on hard problems that lie in the intersection of mathematics, software, and physical systems

Work at Nvidia

nvblox: GPU accelerated real-time reconstruction for robotics

We’re working to bring real-time reconstruction and vision-based navigation to future robots by leveraging Nvidia Jetson devices. Our work is open-source (nvblox), and ready for use with ROS2 (nvblox_ros). This work was featured in a video at GTC 2022.

Selected Publications

A full list of publications may be found my google scholar page.

Freetures: Localization in Signed Distance Function Maps

Alexander Millane, Helen Oleynikova, Christian Lanegger, Jeff Delmerico, Juan Nieto, Roland Siegwart, Marc Pollefeys, and César Cadena
IEEE Robotics and Automation Letters, 2020.

Free‑Space Features: Global Localization in 2D Laser SLAM Using Distance Function Maps

Alexander Millane, Helen Oleynikova, Juan Nieto, Roland Siegwart, and César Cadena
IEEE International Conference on Intelligent Robots and Systems (IROS), 2019

Voxgraph: Globally Consistent, Volumetric Mapping using Signed Distance Function Submaps

Victor Reijgwart*, Alexander Millane*, Helen Oleynikova, Roland Siegwart, César Cadena, Juan Nieto
IEEE Robotics and Automation Letters, 2019

* contributed equally

C-blox: A Scalable and Consistent TSDF-based Dense Mapping Approach

Alexander Millane, Zachary Taylor, Helen Oleynikova, Juan Nieto, Roland Siegwart, César Cadena
IEEE International Conference on Intelligent Robots and Systems (IROS), 2018

Projects

Autonomous Fire-Fighting

In this project we built a system for autonomously finding fires in multi-story buildings as part of our entry to the MBZIRC 2020 international robotics competition. The mission is completed by a collaborating robotic team, consisting of a hexacopter and a tricopter. The approach exploits the mapping and precise control capabilities of each of the vehicles respectively. If you’re interested, check out our video.

Thermal Mapping

In this work we showed a UAV building dense 3D maps, localizing within these maps, and autonomously navigating through narrow spaces to find potential injured people using a thermal camera. We demonstrated this at a search and rescue training site in Switzerland. If you’re interested, check out our video.