2 research outputs found
DeepLocalization: Landmark-based Self-Localization with Deep Neural Networks
We address the problem of vehicle self-localization from multi-modal sensor
information and a reference map. The map is generated off-line by extracting
landmarks from the vehicle's field of view, while the measurements are
collected similarly on the fly. Our goal is to determine the autonomous
vehicle's pose from the landmark measurements and map landmarks. To learn this
mapping, we propose DeepLocalization, a deep neural network that regresses the
vehicle's translation and rotation parameters from unordered and dynamic input
landmarks. The proposed network architecture is robust to changes of the
dynamic environment and can cope with a small number of extracted landmarks.
During the training process we rely on synthetically generated ground-truth. In
our experiments, we evaluate two inference approaches in real-world scenarios.
We show that DeepLocalization can be combined with regular GPS signals and
filtering algorithms such as the extended Kalman filter. Our approach achieves
state-of-the-art accuracy and is about ten times faster than the related work.Comment: Accepted for publication by the IEEE Intelligent Transportation
Systems Conference (ITSC 2019), Auckland, New Zealan
All-Weather sub-50-cm Radar-Inertial Positioning
Deployment of automated ground vehicles beyond the confines of sunny and dry
climes will require sub-lane-level positioning techniques based on radio waves
rather than near-visible-light radiation. Like human sight, lidar and cameras
perform poorly in low-visibility conditions. This paper develops and
demonstrates a novel technique for robust sub-50-cm-accurate urban ground
vehicle positioning based on all-weather sensors. The technique incorporates a
computationally-efficient globally-optimal radar scan batch registration
algorithm into a larger estimation pipeline that fuses data from
commercially-available low-cost automotive radars, low-cost inertial sensors,
vehicle motion constraints, and, when available, precise GNSS measurements.
Performance is evaluated on an extensive and realistic urban data set.
Comparison against ground truth shows that during 60 minutes of GNSS-denied
driving in the urban center of Austin, TX, the technique maintains
95th-percentile errors below 50 cm in horizontal position and 0.5 degrees in
heading.Comment: 17 pages, 12 figures, submitted for review to IEEE Transactions on
Robotics. arXiv admin note: substantial text overlap with arXiv:2005.0070