688 research outputs found
External multi-modal imaging sensor calibration for sensor fusion: A review
Multi-modal data fusion has gained popularity due to its diverse applications, leading to an increased demand for external sensor calibration. Despite several proven calibration solutions, they fail to fully satisfy all the evaluation criteria, including accuracy, automation, and robustness. Thus, this review aims to contribute to this growing field by examining recent research on multi-modal imaging sensor calibration and proposing future research directions. The literature review comprehensively explains the various characteristics and conditions of different multi-modal external calibration methods, including traditional motion-based calibration and feature-based calibration. Target-based calibration and targetless calibration are two types of feature-based calibration, which are discussed in detail. Furthermore, the paper highlights systematic calibration as an emerging research direction. Finally, this review concludes crucial factors for evaluating calibration methods and provides a comprehensive discussion on their applications, with the aim of providing valuable insights to guide future research directions. Future research should focus primarily on the capability of online targetless calibration and systematic multi-modal sensor calibration.Ministerio de Ciencia, Innovación y Universidades | Ref. PID2019-108816RB-I0
X-CAR: An Experimental Vehicle Platform for Connected Autonomy Research Powered by CARMA
Autonomous vehicles promise a future with a safer, cleaner, more efficient,
and more reliable transportation system. However, the current approach to
autonomy has focused on building small, disparate intelligences that are closed
off to the rest of the world. Vehicle connectivity has been proposed as a
solution, relying on a vision of the future where a mix of connected autonomous
and human-driven vehicles populate the road. Developed by the U.S. Department
of Transportation Federal Highway Administration as a reusable, extensible
platform for controlling connected autonomous vehicles, the CARMA Platform is
one of the technologies enabling this connected future. Nevertheless, the
adoption of the CARMA Platform has been slow, with a contributing factor being
the limited, expensive, and somewhat old vehicle configurations that are
officially supported. To alleviate this problem, we propose X-CAR (eXperimental
vehicle platform for Connected Autonomy Research). By implementing the CARMA
Platform on more affordable, high quality hardware, X-CAR aims to increase the
versatility of the CARMA Platform and facilitate its adoption for research and
development of connected driving automation
Multi-Modal 3D Object Detection in Autonomous Driving: a Survey
In the past few years, we have witnessed rapid development of autonomous
driving. However, achieving full autonomy remains a daunting task due to the
complex and dynamic driving environment. As a result, self-driving cars are
equipped with a suite of sensors to conduct robust and accurate environment
perception. As the number and type of sensors keep increasing, combining them
for better perception is becoming a natural trend. So far, there has been no
indepth review that focuses on multi-sensor fusion based perception. To bridge
this gap and motivate future research, this survey devotes to review recent
fusion-based 3D detection deep learning models that leverage multiple sensor
data sources, especially cameras and LiDARs. In this survey, we first introduce
the background of popular sensors for autonomous cars, including their common
data representations as well as object detection networks developed for each
type of sensor data. Next, we discuss some popular datasets for multi-modal 3D
object detection, with a special focus on the sensor data included in each
dataset. Then we present in-depth reviews of recent multi-modal 3D detection
networks by considering the following three aspects of the fusion: fusion
location, fusion data representation, and fusion granularity. After a detailed
review, we discuss open challenges and point out possible solutions. We hope
that our detailed review can help researchers to embark investigations in the
area of multi-modal 3D object detection
IMU-based Online Multi-lidar Calibration
Modern autonomous systems typically use several sensors for perception. For
best performance, accurate and reliable extrinsic calibration is necessary. In
this research, we propose a reliable technique for the extrinsic calibration of
several lidars on a vehicle without the need for odometry estimation or
fiducial markers. First, our method generates an initial guess of the
extrinsics by matching the raw signals of IMUs co-located with each lidar. This
initial guess is then used in ICP and point cloud feature matching which
refines and verifies this estimate. Furthermore, we can use observability
criteria to choose a subset of the IMU measurements that have the highest
mutual information -- rather than comparing all the readings. We have
successfully validated our methodology using data gathered from Scania test
vehicles.Comment: For associated video, see https://youtu.be/HJ0CBWTFOh
High-accuracy patternless calibration of multiple 3D LiDARs for autonomous vehicles
This article proposes a new method for estimating the extrinsic calibration parameters between any pair of multibeam LiDAR sensors on a vehicle. Unlike many state-of-the-art works, this method does not use any calibration pattern or reflective marks placed in the environment to perform the calibration; in addition, the sensors do not need to have overlapping fields of view. An iterative closest point (ICP)-based process is used to determine the values of the calibration parameters, resulting in better convergence and improved accuracy. Furthermore, a setup based on the car learning to act (CARLA) simulator is introduced to evaluate the approach, enabling quantitative assessment with ground-truth data. The results show an accuracy comparable with other approaches that require more complex procedures and have a more restricted range of applicable setups. This work also provides qualitative results on a real setup, where the alignment between the different point clouds can be visually checked. The open-source code is available at https://github.com/midemig/pcd_calib .This work was supported in part by the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with UC3M ("Fostering Young Doctors Research," APBI-CM-UC3M) in the context of the V PRICIT (Research and Technological Innovation Regional Program); and in part by the Spanish Government through Grants ID2021-128327OA-I00 and TED2021-129374A-I00 funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR
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