The $\mathbf{AX} = \mathbf{YB}$ sensor calibration problem is very important in multiple fields such as robotics, computer vision or machine vision. In this type of problem, A and B are parameterizations of rigid body motions, while X and Y is an unknown's transformations. This research proposes a new approach for solving the $\mathbf{AX} = \mathbf{YB}$ sensor calibration problem by mapping the $\boldsymbol{S}\mathbb{E}_{3}$ classic formulation into an orthogonal dual tensors set $\underline{\boldsymbol{S}\mathbb{O}}_{3}$ representation. Using an isomorphism between $\boldsymbol{S}\mathbb{E}_{3}$ and $\underline{\boldsymbol{S}\mathbb{O}}_{3}$ , these solutions are free of coordinates and can easily be put into practice.

A novel solution for $\mathbf{AX}=\mathbf{YB}$ sensor calibration problem using dual Lie algebra

Monocular camera sensors are vital to intelligent vehicle operation and automated driving assistance and are also heavily employed in traffic control infrastructure. Calibrating the monocular camera, though, is time-consuming and often requires significant manual intervention. In this work, we present an extrinsic camera calibration approach that automatizes the parameter estimation by utilizing semantic segmentation information from images and point clouds. Our approach relies on a coarse initial measurement of the camera pose and builds on lidar sensors mounted on a vehicle with high-precision localization to capture a point cloud of the camera environment. Afterward, a mapping between the camera and world coordinate spaces is obtained by performing a lidar-to-camera registration of the semantically segmented sensor data. We evaluate our method on simulated and real-world data to demonstrate low error measurements in the calibration results. Our approach is suitable for infrastructure sensors as well as vehicle sensors, while it does not require motion of the camera platform.

Extrinsic Camera Calibration with Semantic Segmentation

In this paper a large size M-array based on pseudo-random numbers is proposed to implement spatial encoding structured light technique for dynamic 3D reconstruction. By using our technique, more accurate M-array can be generated. The large size M-array benefits in providing more information on object surfaces as more primitives can be contained in the pattern. We first discuss a pseudo-random array and point out problems existing ones. Then, we elaborate a new method to generate a large size M-array using pseudo-random numbers, which eliminates the limitation imposed on the coprime of rows and columns of pseudo-random array. To get more robust structured light system, color points are replaced with geometric symbols since color coded pattern are more sensitive to background and noise. The decoding method, in this research, is also improved, which significantly enhances accuracy of neighborhood finding in uneven or unbalance surfaces. Finally, 3D reconstruction results for cylinder and sculpture are presented to show the validity of the proposed large size M-array and decoding method.

A structured light vision measurement with large size M-array for dynamic scenes

Monocular camera sensors are vital to intelligent vehicle operation and automated driving assistance and are also heavily employed in traffic control infrastructure. Calibrating the monocular camera, though, is time-consuming and often requires significant manual intervention. In this work, we present an extrinsic camera calibration approach that automatizes the parameter estimation by utilizing semantic segmentation information from images and point clouds. Our approach relies on a coarse initial measurement of the camera pose and builds on lidar sensors mounted on a vehicle with high-precision localization to capture a point cloud of the camera environment. Afterward, a mapping between the camera and world coordinate spaces is obtained by performing a lidar-to-camera registration of the semantically segmented sensor data. We evaluate our method on simulated and real-world data to demonstrate low error measurements in the calibration results. Our approach is suitable for infrastructure sensors as well as vehicle sensors, while it does not require motion of the camera platform. 

/pdf/extrinsic-camera-calibration-with-semantic-segmentation-10v0qrx8.pdf

The application of machine vision techniques in Vehicle-to-Everything (V2X) scenarios within Intelligent Connected Systems (ICS) has gained increasing importance with advancements in 6G communication technology. However, the stringent latency and bandwidth requirements of most machine vision applications pose significant challenges to the existing infrastructure. Hence, there is a dearth of prior research examining whether the latency of real applications in ICS aligns with the needs of machine vision scenarios, let alone any performance evaluations conducted in this regard. In this paper, we conduct a comprehensive literature review and proposed a novel machine vision architecture that can analyze traffic data in real-time in the V2X scenario within ICS. Furthermore, based on the end-to-end latency assessment of the system, we outline a plan to optimize the latency as per the requirements of the machine vision application. Our findings show that with appropriate algorithms and architecture, the ICS system can meet the stringent needs of machine vision applications. Our research can provide valuable insights as a guideline on ICS with high latency requirements and therefore pave the way for future explorations in this field. 

Vehicle-Based Machine Vision Approaches in Intelligent Connected System

Belonging to the dynamic and real-time metrology, the key on 3D shape measurement of moving object is one-shot technology including one image acquisition, real-time processing and displaying. According to different speeds of motion, a few structured light methods such as spatial encoding, FTP, phase shifting method and spacetime stereo are introduced and summarized in the form of principle, development, performance and application for shape acquisition of moving object in this paper. Although structured light for measuring 3D shape of moving object is significant and valuable in theory and engineering applications, the research still has some technological challenges and problems to be explored and solved.

3D shape acquisition of moving object based on structured light

Visual multi-object tracking is an important task within the field of computer vision. The goal of this paper is to track a variable number of unknown objects in complex scenes automatically using a moving and un-calibrated camera and it devotes to overcome the challenging problems including illumination and scale variations, viewpoint variations and significant occlusions, etc. In this paper, a binary representation containing color and gradient information is utilized to obtain unique features so that the objects can be easily distinguished from each other in the feature space. In addition, an online learning framework based on a cascaded classifier which is trained and updated in each frame to distinguish the object from the background is proposed for long-term tracking. The experimental results on both quantitative evaluations and multi-object tracking show that this approach yields an accurate and robust tracking performance in a large variety of complex scenarios.

http://ieeexplore.ieee.org/iel7/6731716/6743112/06743213.pdf

Online learning of cascaded classifier designed for multi-object tracking

In order to enhance the accuracy and real-time performance of mobile robot in complex environment, a pedestrian detection and tracking method combined with infrared and visible image features is proposed. Firstly, a 4 degree of freedom multispectral vision platform is designed. Secondly, a coarse-fine pedestrian detection and tracking is implemented by using census transform histogram (CENTRIST) descriptor and kernel correlation filtering (KCF). Then, a head-eye coordination control strategy for multispectral vision platform is established. Finally, the accuracy of pedestrian detection and tracking algorithm as well as the effectiveness of the active visual tracking system are verified. The experimental results clearly demonstrate the effectiveness of our multispectral visual tracking system compared to that based on visible or infrared spectrum singly, and the success rate of pedestrian tracking based on multispectral images is increased by 28.9% and 10.1% respectively.

Combined Thermal Infrared and Visible Cameras for Pedestrian Detection and Tracking

In order to overcome the restrictions of traditional robot-sensor calibration method which solve the tool-camera transformation and robot-world transformation rely on calibration target, a calibration-free approach that solve the robot-sensor calibration problem of the form AX = YB based on Second-Order Cone Programming is proposed. First, a Structure-From-Motion approach was used to recover the camera motion matrix up to scaling. Then rotation and translation matrix in calibration equation were parameterized by dual quaternion theory. Finally, the Second-Order Cone Programming method was used to simultaneously solve the optimal solution of camera motion matrix scale factor, the robot-world and hand-eye relation. The experimental results indicate that the calibration precision of rotation relative error is 3.998% and the translation relative error is 0.117% in defect of calibration target as 3D benchmark. Compared with similar methods, the proposed method can effectively improve the calibration accuracy of the robot-world and hand-eye relation, and extend the application field of robot-sensor calibration method.

/pdf/calibration-free-robot-sensor-calibration-approach-based-on-2ieyg3rccx.pdf

Calibration-Free Robot-Sensor Calibration approach based on Second-Order Cone Programming

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Papers

3D shape acquisition of moving object based on structured light

Online learning of cascaded classifier designed for multi-object tracking

Combined Thermal Infrared and Visible Cameras for Pedestrian Detection and Tracking

Calibration-Free Robot-Sensor Calibration approach based on Second-Order Cone Programming