594 research outputs found
Reflectance Intensity Assisted Automatic and Accurate Extrinsic Calibration of 3D LiDAR and Panoramic Camera Using a Printed Chessboard
This paper presents a novel method for fully automatic and convenient
extrinsic calibration of a 3D LiDAR and a panoramic camera with a normally
printed chessboard. The proposed method is based on the 3D corner estimation of
the chessboard from the sparse point cloud generated by one frame scan of the
LiDAR. To estimate the corners, we formulate a full-scale model of the
chessboard and fit it to the segmented 3D points of the chessboard. The model
is fitted by optimizing the cost function under constraints of correlation
between the reflectance intensity of laser and the color of the chessboard's
patterns. Powell's method is introduced for resolving the discontinuity problem
in optimization. The corners of the fitted model are considered as the 3D
corners of the chessboard. Once the corners of the chessboard in the 3D point
cloud are estimated, the extrinsic calibration of the two sensors is converted
to a 3D-2D matching problem. The corresponding 3D-2D points are used to
calculate the absolute pose of the two sensors with Unified Perspective-n-Point
(UPnP). Further, the calculated parameters are regarded as initial values and
are refined using the Levenberg-Marquardt method. The performance of the
proposed corner detection method from the 3D point cloud is evaluated using
simulations. The results of experiments, conducted on a Velodyne HDL-32e LiDAR
and a Ladybug3 camera under the proposed re-projection error metric,
qualitatively and quantitatively demonstrate the accuracy and stability of the
final extrinsic calibration parameters.Comment: 20 pages, submitted to the journal of Remote Sensin
A mask-based approach for the geometric calibration of thermal-infrared cameras
Accurate and efficient thermal-infrared (IR) camera calibration is important for advancing computer vision research within the thermal modality. This paper presents an approach for geometrically calibrating individual and multiple cameras in both the thermal and visible modalities. The proposed technique can be used to correct for lens distortion and to simultaneously reference both visible and thermal-IR cameras to a single coordinate frame. The most popular existing approach for the geometric calibration of thermal cameras uses a printed chessboard heated by a flood lamp and is comparatively inaccurate and difficult to execute. Additionally, software toolkits provided for calibration either are unsuitable for this task or require substantial manual intervention. A new geometric mask with high thermal contrast and not requiring a flood lamp is presented as an alternative calibration pattern. Calibration points on the pattern are then accurately located using a clustering-based algorithm which utilizes the maximally stable extremal region detector. This algorithm is integrated into an automatic end-to-end system for calibrating single or multiple cameras. The evaluation shows that using the proposed mask achieves a mean reprojection error up to 78% lower than that using a heated chessboard. The effectiveness of the approach is further demonstrated by using it to calibrate two multiple-camera multiple-modality setups. Source code and binaries for the developed software are provided on the project Web site
Real-Time fusion of visual images and laser data images for safe navigation in outdoor environments
[EN]In recent years, two dimensional laser range finders mounted on vehicles is becoming a
fruitful solution to achieve safety and environment recognition requirements (Keicher &
Seufert, 2000), (Stentz et al., 2002), (DARPA, 2007). They provide real-time accurate range
measurements in large angular fields at a fixed height above the ground plane, and enable
robots and vehicles to perform more confidently a variety of tasks by fusing images from
visual cameras with range data (Baltzakis et al., 2003). Lasers have normally been used in
industrial surveillance applications to detect unexpected objects and persons in indoor
environments. In the last decade, laser range finder are moving from indoor to outdoor rural
and urban applications for 3D imaging (Yokota et al., 2004), vehicle guidance (Barawid et
al., 2007), autonomous navigation (Garcia-PĂ©rez et al., 2008), and objects recognition and
classification (Lee & Ehsani, 2008), (Edan & Kondo, 2009), (Katz et al., 2010). Unlike
industrial applications, which deal with simple, repetitive and well-defined objects, cameralaser
systems on board off-road vehicles require advanced real-time techniques and
algorithms to deal with dynamic unexpected objects. Natural environments are complex
and loosely structured with great differences among consecutive scenes and scenarios.
Vision systems still present severe drawbacks, caused by lighting variability that depends
on unpredictable weather conditions. Camera-laser objects feature fusion and classification
is still a challenge within the paradigm of artificial perception and mobile robotics in
outdoor environments with the presence of dust, dirty, rain, and extreme temperature and
humidity. Real time relevant objects perception, task driven, is a main issue for subsequent
actions decision in safe unmanned navigation. In comparison with industrial automation
systems, the precision required in objects location is usually low, as it is the speed of most
rural vehicles that operate in bounded and low structured outdoor environments.
To this aim, current work is focused on the development of algorithms and strategies for
fusing 2D laser data and visual images, to accomplish real-time detection and classification
of unexpected objects close to the vehicle, to guarantee safe navigation. Next, class
information can be integrated within the global navigation architecture, in control modules,
such as, stop, obstacle avoidance, tracking or mapping.Section 2 includes a description of the commercial vehicle, robot-tractor DEDALO and the
vision systems on board. Section 3 addresses some drawbacks in outdoor perception.
Section 4 analyses the proposed laser data and visual images fusion method, focused in the
reduction of the visual image area to the region of interest wherein objects are detected by
the laser. Two methods of segmentation are described in Section 5, to extract the shorter area
of the visual image (ROI) resulting from the fusion process. Section 6 displays the colour
based classification results of the largest segmented object in the region of interest. Some
conclusions are outlined in Section 7, and acknowledgements and references are displayed
in Section 8 and Section 9.projects: CICYT- DPI-2006-14497 by the Science
and Innovation Ministry, ROBOCITY2030 I y II: Service Robots-PRICIT-CAM-P-DPI-000176-
0505, and SEGVAUTO: Vehicle Safety-PRICIT-CAM-S2009-DPI-1509 by Madrid State
Government.Peer reviewe
Targetless Extrinsic Calibration of Stereo Cameras, Thermal Cameras, and Laser Sensors in the Wild
The fusion of multi-modal sensors has become increasingly popular in
autonomous driving and intelligent robots since it can provide richer
information than any single sensor, enhance reliability in complex
environments. Multi-sensor extrinsic calibration is one of the key factors of
sensor fusion. However, such calibration is difficult due to the variety of
sensor modalities and the requirement of calibration targets and human labor.
In this paper, we demonstrate a new targetless cross-modal calibration
framework by focusing on the extrinsic transformations among stereo cameras,
thermal cameras, and laser sensors. Specifically, the calibration between
stereo and laser is conducted in 3D space by minimizing the registration error,
while the thermal extrinsic to the other two sensors is estimated by optimizing
the alignment of the edge features. Our method requires no dedicated targets
and performs the multi-sensor calibration in a single shot without human
interaction. Experimental results show that the calibration framework is
accurate and applicable in general scenes.Comment: This work has been submitted to the IEEE for possible publication.
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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, InnovaciĂłn y Universidades | Ref. PID2019-108816RB-I0
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