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James Keller

4 accepted papers

2019

MAVNet: An Effective Semantic Segmentation Micro-Network for MAV-Based Tasks

RA-L 2019

Real-time semantic image segmentation on platforms subject to size, weight, and power constraints is a key area of interest for air surveillance and inspection. In this letter, we propose MAVNet: a small, light-weight, deep neural network for real-time semantic segmentation on micro aerial vehicles

Cited by 38Source ↗code ↗Scholar ↗
2018

Spatio-Temporally Smooth Local Mapping and State Estimation Inside Generalized Cylinders With Micro Aerial Vehicles

RA-L 2018

In this letter, we consider state estimation and local mapping with a micro aerial vehicle inside a tunnel that can be modeled as a generalized cylinder, using a three-dimensional lidar and an inertial measurement unit. This axisymmetric environment poses unique challenges in terms of localization a

Cited by 17Source ↗Scholar ↗
2017

Autonomous Navigation and Mapping for Inspection of Penstocks and Tunnels With MAVs

RA-L 2017

In this paper, we address the estimation, control, navigation and mapping problems to achieve autonomous inspection of penstocks and tunnels using aerial vehicles with on-board sensing and computation. Penstocks and tunnels have the shape of a generalized cylinder. They are generally dark and featur

Cited by 135Source ↗Scholar ↗
2016

Towards fully autonomous visual inspection of dark featureless dam penstocks using MAVs

IROS 2016poster

In the last decade, multi-rotor Micro Aerial Vehicles (MAVs) have attracted great attention from robotics researchers. Offering affordable agility and maneuverability, multi-rotor aircrafts have become the most commonly used platforms for robotics applications. Amongst the most promising application…

Cited by 47Source ↗Scholar ↗