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

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

Hand-eye calibration is the first step of any vision system for robot manipulators. With 3D vision sensors becoming popular recently, how they can be used for real-time accurate hand-eye calibration is awaiting to be studied. In this paper, we propose a generic method for both eye-to-hand and eye-in-hand calibration using a 3D sensor. By exploiting a given feature point and the flange center point, we can formulate the two hand-eye calibration problems as one simple form: $p^{\prime}=Rp+t$ . Herein, the flange center point is found by fitting the different locations of the feature point to a sphere surface. To do the sphere fitting and handle the above equation, we present a closed-form solution and two iterative solutions by least square method and random sample consensus algorithm. As traditional calibration marker is unnecessary, we can eliminate large measurement errors caused by the pose estimation of the marker. The performance of our method is simulated by two steps of calibration and validated with autonomous experiments. This work can be done instantaneously after the robot finishes the movements, which represents a simple and real-time step towards accurate hand-eye calibration in robotic applications.

A Simple and Generic Method for Real-time Accurate Hand-eye Calibration Using a 3D Vision Sensor

Overview of Motion Control on Bionic Eyes

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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Combined Thermal Infrared and Visible Cameras for Pedestrian Detection and Tracking

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