21,316 research outputs found
CNN for IMU Assisted Odometry Estimation using Velodyne LiDAR
We introduce a novel method for odometry estimation using convolutional
neural networks from 3D LiDAR scans. The original sparse data are encoded into
2D matrices for the training of proposed networks and for the prediction. Our
networks show significantly better precision in the estimation of translational
motion parameters comparing with state of the art method LOAM, while achieving
real-time performance. Together with IMU support, high quality odometry
estimation and LiDAR data registration is realized. Moreover, we propose
alternative CNNs trained for the prediction of rotational motion parameters
while achieving results also comparable with state of the art. The proposed
method can replace wheel encoders in odometry estimation or supplement missing
GPS data, when the GNSS signal absents (e.g. during the indoor mapping). Our
solution brings real-time performance and precision which are useful to provide
online preview of the mapping results and verification of the map completeness
in real time
Light field super resolution through controlled micro-shifts of light field sensor
Light field cameras enable new capabilities, such as post-capture refocusing
and aperture control, through capturing directional and spatial distribution of
light rays in space. Micro-lens array based light field camera design is often
preferred due to its light transmission efficiency, cost-effectiveness and
compactness. One drawback of the micro-lens array based light field cameras is
low spatial resolution due to the fact that a single sensor is shared to
capture both spatial and angular information. To address the low spatial
resolution issue, we present a light field imaging approach, where multiple
light fields are captured and fused to improve the spatial resolution. For each
capture, the light field sensor is shifted by a pre-determined fraction of a
micro-lens size using an XY translation stage for optimal performance
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