27 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
Open-Source LiDAR Time Synchronization System by Mimicking GPS-clock
Time synchronization of multiple sensors is one of the main issues when
building sensor networks. Data fusion algorithms and their applications, such
as LiDAR-IMU Odometry (LIO), rely on precise timestamping. We introduce
open-source LiDAR to inertial measurement unit (IMU) hardware time
synchronization system, which could be generalized to multiple sensors such as
cameras, encoders, other LiDARs, etc. The system mimics a GPS-supplied clock
interface by a microcontroller-powered platform and provides 1 microsecond
synchronization precision. In addition, we conduct an evaluation of the system
precision comparing to other synchronization methods, including timestamping
provided by ROS software and LiDAR inner clock, showing clear advantages over
both baseline methods.Comment: IEEE Sensors 2021 Conferenc
Metric Monocular Localization Using Signed Distance Fields
Metric localization plays a critical role in vision-based navigation. For
overcoming the degradation of matching photometry under appearance changes,
recent research resorted to introducing geometry constraints of the prior scene
structure. In this paper, we present a metric localization method for the
monocular camera, using the Signed Distance Field (SDF) as a global map
representation. Leveraging the volumetric distance information from SDFs, we
aim to relax the assumption of an accurate structure from the local Bundle
Adjustment (BA) in previous methods. By tightly coupling the distance factor
with temporal visual constraints, our system corrects the odometry drift and
jointly optimizes global camera poses with the local structure. We validate the
proposed approach on both indoor and outdoor public datasets. Compared to the
state-of-the-art methods, it achieves a comparable performance with a minimal
sensor configuration.Comment: Accepted to 2019 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS
DSEC: A Stereo Event Camera Dataset for Driving Scenarios
Once an academic venture, autonomous driving has received unparalleled
corporate funding in the last decade. Still, the operating conditions of
current autonomous cars are mostly restricted to ideal scenarios. This means
that driving in challenging illumination conditions such as night, sunrise, and
sunset remains an open problem. In these cases, standard cameras are being
pushed to their limits in terms of low light and high dynamic range
performance. To address these challenges, we propose, DSEC, a new dataset that
contains such demanding illumination conditions and provides a rich set of
sensory data. DSEC offers data from a wide-baseline stereo setup of two color
frame cameras and two high-resolution monochrome event cameras. In addition, we
collect lidar data and RTK GPS measurements, both hardware synchronized with
all camera data. One of the distinctive features of this dataset is the
inclusion of high-resolution event cameras. Event cameras have received
increasing attention for their high temporal resolution and high dynamic range
performance. However, due to their novelty, event camera datasets in driving
scenarios are rare. This work presents the first high-resolution, large-scale
stereo dataset with event cameras. The dataset contains 53 sequences collected
by driving in a variety of illumination conditions and provides ground truth
disparity for the development and evaluation of event-based stereo algorithms.Comment: IEEE Robotics and Automation Letter