  In this paper, we introduce IDOL, an optimization-based framework for IMU-DVS
Odometry using Lines. Event cameras, also called Dynamic Vision Sensors (DVSs),
generate highly asynchronous streams of events triggered upon illumination
changes for each individual pixel. This novel paradigm presents advantages in
low illumination conditions and high-speed motions. Nonetheless, this
unconventional sensing modality brings new challenges to perform scene
reconstruction or motion estimation. The proposed method offers to leverage a
continuous-time representation of the inertial readings to associate each event
with timely accurate inertial data. The method's front-end extracts event
clusters that belong to line segments in the environment whereas the back-end
estimates the system's trajectory alongside the lines' 3D position by
minimizing point-to-line distances between individual events and the lines'
projection in the image space. A novel attraction/repulsion mechanism is
presented to accurately estimate the lines' extremities, avoiding their
explicit detection in the event data. The proposed method is benchmarked
against a state-of-the-art frame-based visual-inertial odometry framework using
public datasets. The results show that IDOL performs at the same order of
magnitude on most datasets and even shows better orientation estimates. These
findings can have a great impact on new algorithms for DVS.
