112 research outputs found
3D Lidar-IMU Calibration Based on Upsampled Preintegrated Measurements for Motion Distortion Correction
© 2018 IEEE. In this paper, we present a probabilistic framework to recover the extrinsic calibration parameters of a lidar-IMU sensing system. Unlike global-shutter cameras, lidars do not take single snapshots of the environment. Instead, lidars collect a succession of 3D-points generally grouped in scans. If these points are assumed to be expressed in a common frame, this becomes an issue when the sensor moves rapidly in the environment causing motion distortion. The fundamental idea of our proposed framework is to use preintegration over interpolated inertial measurements to characterise the motion distortion in each lidar scan. Moreover, by using a set of planes as a calibration target, the proposed method makes use of lidar point-to-plane distances to jointly calibrate and localise the system using on-manifold optimisation. The calibration does not rely on a predefined target as arbitrary planes are detected and modelled in the first lidar scan. Simulated and real data are used to show the effectiveness of the proposed method
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
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
CaLib: Simple and Accurate LiDAR-RGB Calibration using Small Common Markers
In many fields of robotics, knowing the relative position and orientation
between two sensors is a mandatory precondition to operate with multiple
sensing modalities. In this context, the pair LiDAR-RGB cameras offer
complementary features: LiDARs yield sparse high quality range measurements,
while RGB cameras provide a dense color measurement of the environment.
Existing techniques often rely either on complex calibration targets that are
expensive to obtain, or extracted virtual correspondences that can hinder the
estimate's accuracy. In this paper we address the problem of LiDAR-RGB
calibration using typical calibration patterns (i.e. A3 chessboard) with
minimal human intervention. Our approach exploits the planarity of the target
to find correspondences between the sensors measurements, leading to features
that are robust to LiDAR noise.
Moreover, we estimate a solution by solving a joint non-linear optimization
problem. We validated our approach by carrying on quantitative and comparative
experiments with other state-of-the-art approaches. Our results show that our
simple schema performs on par or better than other approches using complex
calibration targets. Finally, we release an open-source C++ implementation at
\url{https://github.com/srrg-sapienza/ca2lib}Comment: 7 pages, 10 figure
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