Calibration of a Rotating or Revolving Platform with a LiDAR Sensor

Abstract

Perceiving its environment in 3D is an important ability for a modern robot. Today, this is often done using LiDARs which come with a strongly limited field of view (FOV), however. To extend their FOV, the sensors are mounted on driving vehicles in several different ways. This allows 3D perception even with 2D LiDARs if a corresponding localization system or technique is available. Another popular way to gain most information of the scanners is to mount them on a rotating carrier platform. In this way, their measurements in different directions can be collected and transformed into a common frame, in order to achieve a nearly full spherical perception. However, this is only possible if the kinetic chains of the platforms are known exactly, that is, if the LiDAR pose w.r.t. to its rotation center is well known. The manual measurement of these chains is often very cumbersome or sometimes even impossible to do with the necessary precision. Our paper proposes a method to calibrate the extrinsic LiDAR parameters by decoupling the rotation from the full six degrees of freedom transform and optimizing both separately. Thus, one error measure for the orientation and one for the translation with known orientation are minimized subsequently with a combination of a consecutive grid search and a gradient descent. Both error measures are inferred from spherical calibration targets. Our experiments with the method suggest that the main influences on the calibration results come from the the distance to the calibration targets, the accuracy of their center point estimation and the search grid resolution. However, our proposed calibration method improves the extrinsic parameters even with unfavourable configurations and from inaccurate initial pose guesses.

Approach to Automated Visual Inspection of Objects Based on Artificial Intelligence

“…Part (4) represents the dimensions of the tire. Part (5), which consists of a maximum of four characters, represents a space for the manufacturer to indicate a more detailed specification of the type or selected characteristics of the tire. The last four digits (6) indicate the week and year of manufacture of a particular piece.…”

“…Modern advanced sensing systems based on "smart camera sensors" have been quickly spread into many fields of industry, such as general process control [3], general object identification and recognition, reading texts and codes, face recognition [4], robot control via gestures and general pose control of robots using visual servoing [5,6], visual navigation of single/multiple mobile robot/-s, collision detection and perception [7] and many others. Smart sensors are key elements for visual inspection or visual navigation tasks that have previously been undertaken manually by human operators.…”

Case study: Performance analysis and development of robotized screwing application with integrated vision sensing system for automotive industry

“…Among the advanced camera systems, we can classify above all visual navigation of mobile robots and self-driving cars, human body gesture or face keypoints recognition, and so on. The application of MV system in autonomous navigation of mobile robots and self-driving cars is mentioned in authors such as Kim, 11 Claer, 25 and others. Jeong-Hyun Kim 11 proposed the so-called Self-Organizing Map algorithm to obtain suitable data about the position of objects in space based on depth data from a stereo camera.…”

“…In general, this type of sensor uses an indirect method of sensing the world based on the 3D space transformation into 2D image information, whether perpendicular or perspective projection is defined. Mario Claer 25 pointed out that it is becoming increasingly important for the robotic system to be able to perceive its surroundings in 3D in specific areas. Scanner based on light detection and ranging method for measuring distance of obstacles (LiDARs) and other kind of 3D scanners are therefore often used for similar tasks, even though their field of vision is smaller.…”