  Deep learning based LiDAR odometry (LO) estimation attracts increasing
research interests in the field of autonomous driving and robotics. Existing
works feed consecutive LiDAR frames into neural networks as point clouds and
match pairs in the learned feature space. In contrast, motivated by the success
of image based feature extractors, we propose to transfer the LiDAR frames to
image space and reformulate the problem as image feature extraction. With the
help of scale-invariant feature transform (SIFT) for feature extraction, we are
able to generate matched keypoint pairs (MKPs) that can be precisely returned
to the 3D space. A convolutional neural network pipeline is designed for LiDAR
odometry estimation by extracted MKPs. The proposed scheme, namely LodoNet, is
then evaluated in the KITTI odometry estimation benchmark, achieving on par
with or even better results than the state-of-the-art.
