58 research outputs found
LO-Net: Deep Real-time Lidar Odometry
We present a novel deep convolutional network pipeline, LO-Net, for real-time
lidar odometry estimation. Unlike most existing lidar odometry (LO) estimations
that go through individually designed feature selection, feature matching, and
pose estimation pipeline, LO-Net can be trained in an end-to-end manner. With a
new mask-weighted geometric constraint loss, LO-Net can effectively learn
feature representation for LO estimation, and can implicitly exploit the
sequential dependencies and dynamics in the data. We also design a scan-to-map
module, which uses the geometric and semantic information learned in LO-Net, to
improve the estimation accuracy. Experiments on benchmark datasets demonstrate
that LO-Net outperforms existing learning based approaches and has similar
accuracy with the state-of-the-art geometry-based approach, LOAM
Robust and Fast 3D Scan Alignment using Mutual Information
This paper presents a mutual information (MI) based algorithm for the
estimation of full 6-degree-of-freedom (DOF) rigid body transformation between
two overlapping point clouds. We first divide the scene into a 3D voxel grid
and define simple to compute features for each voxel in the scan. The two scans
that need to be aligned are considered as a collection of these features and
the MI between these voxelized features is maximized to obtain the correct
alignment of scans. We have implemented our method with various simple point
cloud features (such as number of points in voxel, variance of z-height in
voxel) and compared the performance of the proposed method with existing
point-to-point and point-to- distribution registration methods. We show that
our approach has an efficient and fast parallel implementation on GPU, and
evaluate the robustness and speed of the proposed algorithm on two real-world
datasets which have variety of dynamic scenes from different environments
Multi-modal Sensor Registration for Vehicle Perception via Deep Neural Networks
The ability to simultaneously leverage multiple modes of sensor information
is critical for perception of an automated vehicle's physical surroundings.
Spatio-temporal alignment of registration of the incoming information is often
a prerequisite to analyzing the fused data. The persistence and reliability of
multi-modal registration is therefore the key to the stability of decision
support systems ingesting the fused information. LiDAR-video systems like on
those many driverless cars are a common example of where keeping the LiDAR and
video channels registered to common physical features is important. We develop
a deep learning method that takes multiple channels of heterogeneous data, to
detect the misalignment of the LiDAR-video inputs. A number of variations were
tested on the Ford LiDAR-video driving test data set and will be discussed. To
the best of our knowledge the use of multi-modal deep convolutional neural
networks for dynamic real-time LiDAR-video registration has not been presented.Comment: 7 pages, double column, IEEE format, accepted at IEEE HPEC 201
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