1,474 research outputs found
Tightly Coupled 3D Lidar Inertial Odometry and Mapping
Ego-motion estimation is a fundamental requirement for most mobile robotic
applications. By sensor fusion, we can compensate the deficiencies of
stand-alone sensors and provide more reliable estimations. We introduce a
tightly coupled lidar-IMU fusion method in this paper. By jointly minimizing
the cost derived from lidar and IMU measurements, the lidar-IMU odometry (LIO)
can perform well with acceptable drift after long-term experiment, even in
challenging cases where the lidar measurements can be degraded. Besides, to
obtain more reliable estimations of the lidar poses, a rotation-constrained
refinement algorithm (LIO-mapping) is proposed to further align the lidar poses
with the global map. The experiment results demonstrate that the proposed
method can estimate the poses of the sensor pair at the IMU update rate with
high precision, even under fast motion conditions or with insufficient
features.Comment: Accepted by ICRA 201
A General Framework for Flexible Multi-Cue Photometric Point Cloud Registration
The ability to build maps is a key functionality for the majority of mobile
robots. A central ingredient to most mapping systems is the registration or
alignment of the recorded sensor data. In this paper, we present a general
methodology for photometric registration that can deal with multiple different
cues. We provide examples for registering RGBD as well as 3D LIDAR data. In
contrast to popular point cloud registration approaches such as ICP our method
does not rely on explicit data association and exploits multiple modalities
such as raw range and image data streams. Color, depth, and normal information
are handled in an uniform manner and the registration is obtained by minimizing
the pixel-wise difference between two multi-channel images. We developed a
flexible and general framework and implemented our approach inside that
framework. We also released our implementation as open source C++ code. The
experiments show that our approach allows for an accurate registration of the
sensor data without requiring an explicit data association or model-specific
adaptations to datasets or sensors. Our approach exploits the different cues in
a natural and consistent way and the registration can be done at framerate for
a typical range or imaging sensor.Comment: 8 page
A LiDAR-Inertial SLAM Tightly-Coupled with Dropout-Tolerant GNSS Fusion for Autonomous Mine Service Vehicles
Multi-modal sensor integration has become a crucial prerequisite for the
real-world navigation systems. Recent studies have reported successful
deployment of such system in many fields. However, it is still challenging for
navigation tasks in mine scenes due to satellite signal dropouts, degraded
perception, and observation degeneracy. To solve this problem, we propose a
LiDAR-inertial odometry method in this paper, utilizing both Kalman filter and
graph optimization. The front-end consists of multiple parallel running
LiDAR-inertial odometries, where the laser points, IMU, and wheel odometer
information are tightly fused in an error-state Kalman filter. Instead of the
commonly used feature points, we employ surface elements for registration. The
back-end construct a pose graph and jointly optimize the pose estimation
results from inertial, LiDAR odometry, and global navigation satellite system
(GNSS). Since the vehicle has a long operation time inside the tunnel, the
largely accumulated drift may be not fully by the GNSS measurements. We hereby
leverage a loop closure based re-initialization process to achieve full
alignment. In addition, the system robustness is improved through handling data
loss, stream consistency, and estimation error. The experimental results show
that our system has a good tolerance to the long-period degeneracy with the
cooperation different LiDARs and surfel registration, achieving meter-level
accuracy even for tens of minutes running during GNSS dropouts
Towards High-Performance Solid-State-LiDAR-Inertial Odometry and Mapping
We present a novel tightly-coupled LiDAR-inertial odometry and mapping scheme
for both solid-state and mechanical LiDARs. As frontend, a feature-based
lightweight LiDAR odometry provides fast motion estimates for adaptive keyframe
selection. As backend, a hierarchical keyframe-based sliding window
optimization is performed through marginalization for directly fusing IMU and
LiDAR measurements. For the Livox Horizon, a newly released solid-state LiDAR,
a novel feature extraction method is proposed to handle its irregular scan
pattern during preprocessing. LiLi-OM (Livox LiDAR-inertial odometry and
mapping) is real-time capable and achieves superior accuracy over
state-of-the-art systems for both LiDAR types on public data sets of mechanical
LiDARs and in experiments using the Livox Horizon. Source code and recorded
experimental data sets are available on Github.Comment: 15 page
Present and Future of SLAM in Extreme Underground Environments
This paper reports on the state of the art in underground SLAM by discussing
different SLAM strategies and results across six teams that participated in the
three-year-long SubT competition. In particular, the paper has four main goals.
First, we review the algorithms, architectures, and systems adopted by the
teams; particular emphasis is put on lidar-centric SLAM solutions (the go-to
approach for virtually all teams in the competition), heterogeneous multi-robot
operation (including both aerial and ground robots), and real-world underground
operation (from the presence of obscurants to the need to handle tight
computational constraints). We do not shy away from discussing the dirty
details behind the different SubT SLAM systems, which are often omitted from
technical papers. Second, we discuss the maturity of the field by highlighting
what is possible with the current SLAM systems and what we believe is within
reach with some good systems engineering. Third, we outline what we believe are
fundamental open problems, that are likely to require further research to break
through. Finally, we provide a list of open-source SLAM implementations and
datasets that have been produced during the SubT challenge and related efforts,
and constitute a useful resource for researchers and practitioners.Comment: 21 pages including references. This survey paper is submitted to IEEE
Transactions on Robotics for pre-approva
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