Timo Horstschaefer — accepted AI-conference papers

2018

Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High-Speed Scenarios

Antoni Rosinol Vidal, Henri Rebecq, Timo Horstschaefer, Davide Scaramuzza

RA-L 2018

Event cameras are bioinspired vision sensors that output pixel-level brightness changes instead of standard intensity frames. These cameras do not suffer from motion blur and have a very high dynamic range, which enables them to provide reliable visual information during high-speed motions or in sce

Cited by 516 Source ↗Scholar ↗

2017

EVO: A Geometric Approach to Event-Based 6-DOF Parallel Tracking and Mapping in Real Time

Henri Rebecq, Timo Horstschaefer, Guillermo Gallego, Davide Scaramuzza

RA-L 2017

We present EVO, an event-based visual odometry algorithm. Our algorithm successfully leverages the outstanding properties of event cameras to track fast camera motions while recovering a semidense three-dimensional (3-D) map of the environment. The implementation runs in real time on a standard CPU

Cited by 388 Source ↗Scholar ↗