Devices, methods, and software are disclosed for determining dimensions of a physical object using a mobile computer equipped with a motion sensing device. In an illustrative embodiment, the mobile computer can comprise a microprocessor, a memory, a user interface, a motion sensing device, and a dimensioning program executable by the microprocessor. The processor can be in communicative connection with executable instructions for enabling the processor for various steps. One step includes initiating a trajectory tracking mode responsive to receiving a first user interface action. Another step includes tracking the mobile computer's trajectory along a surface of a physical object by storing in the memory a plurality of motion sensing data items outputted by the motion sensing device. Another step includes exiting the trajectory tracking mode responsive to receiving a second user interface action. Another step includes calculating three dimensions of a minimum bounding box corresponding to the physical object.

Measuring object dimensions using mobile computer

Systems and methods for volume dimensioning packages are provided. A method of operating a volume dimensioning system may include the receipt of image data of an area at least a first three-dimensional object to be dimensioned from a first point of view as captured using at least one image sensor. The system can determine from the received image data a number of features in three dimensions of the first three-dimensional object. Based at least on part on the determined features of the first three-dimensional object, the system can fit a first three-dimensional packaging wireframe model about the first three-dimensional object. The system can display of an image of the first three-dimensional packaging wireframe model fitted about an image of the first three-dimensional object on a display device.

Volume dimensioning systems and methods

A terminal for measuring at least one dimension of an object includes at least one imaging subsystem and an actuator. The at least one imaging subsystem includes an imaging optics assembly operable to focus an image onto an image sensor array. The imaging optics assembly has an optical axis. The actuator is operably connected to the at least one imaging subsystem for moving an angle of the optical axis relative to the terminal. The terminal is adapted to obtain first image data of the object and is operable to determine at least one of a height, a width, and a depth dimension of the object based on effecting the actuator to change the angle of the optical axis relative to the terminal to align the object in second image data with the object in the first image data, the second image data being different from the first image data.

Terminals and methods for dimensioning objects

A method is presented for correcting errors in a 3D scanner. Measurement errors in the 3D scanner are determined by scanning each of a plurality of calibration objects in each of a plurality of sectors in the 3D scanner's field of view. The calibration objects have a known height, a known width, and a known length. The measurements taken by the 3D scanner are compared to the known dimensions to derive a measurement error for each dimension in each sector. An estimated measurement error is calculated based on scans of each of the plurality of calibration objects. When scanning target objects in a given sector, the estimated measurement error for that sector is used to correct measurements obtained by the 3D scanner.

Method of error correction for 3D imaging device

Multisensor data fusion in dimensional metrology

The kinematic modeling of articulated arm coordinate measuring machines (AACMM) has inherited both the previous developments in the field of robot arms and manipulators, and their calibration and parameter identification techniques, given the similarity of their mechanical characteristics. The different accuracy and repeatability requirements of both systems make it necessary to consider different identification techniques covering the characteristic operational parameters in each case. This paper presents a new data capture technique for subsequent identification of an AACMM kinematic model parameters, using nominal data reached by a ball bar gauge, along with the algorithm and objective functions used, based on a new approach including terms regarding measurement accuracy and repeatability. Thus, an estimation and error measurement correction method using a repeatability error model based on Fourier polynomials is derived from the identification scheme. A Sterling series FARO arm with 1.5 m long and 6 degrees of freedom (dof) was used to carry out experimental tests in order to evaluate the efficiency of the techniques presented, showing improved accuracy and repeatability performance.

Kinematic parameter estimation technique for calibration and repeatability improvement of articulated arm coordinate measuring machines

This paper presents a new method for volumetric verification of machine tools. Beyond the consideration of a particular machine, a general verification methodology is presented based on the type of machine to verify the number and movement of axes and different techniques that can be used. A scheme and kinematic model with the inclusion of the measurement system depending on the kinematics of the machine are presented. The model describes the geometry and kinematics of a real milling machine based on a parametric synthetic data generator, which generates a test with known geometric errors and noise to enable a study of different optimisation techniques and models. Similarly, different errors identification techniques and volumetric verification models are presented and analysed. The paper shows the improvement that occurs in verification by considering optimisation phases, the appropriateness of using new techniques of feedback, and the influence of optimisation parameters. Chebyshev polynomials and its characteristics are presented, as well as a regression function for the new verification model. The new developed technique allows the characterisation of the different errors in the whole workspace of the machine and in less time than direct verification methods.

Identification strategy of error parameter in volumetric error compensation of machine tool based on laser tracker measurements

Stereo vision for 3D measurement: accuracy analysis, calibration and industrial applications

A technique for intrinsic and extrinsic calibration of laser line scanners, also called laser triangulation sensors (LTSs), for integration in a coordinate measuring machine (CMM) is presented in this paper. Setting out from the modeling of a commercial LTS for use in a CMM and the algorithms implemented for image capture and processing, with the use of a gauge object, a one-step calibration procedure has been developed to obtain both intrinsic parameters?laser plane, CCD sensor and camera geometry?and extrinsic parameters which relate the LTS's reference system to the CMM's reference system, integrating both mathematical models. This method performs both calibrations in a single step, thus avoiding the digitalization of a reference sphere in order to obtain the extrinsic parameters, or optimization procedures subsequent to LTS calibration. The results obtained in accuracy and repeatability tests performed on gauge geometric primitives attest to the viability of this technique for the integration of LTSs in CMMs.

A one-step intrinsic and extrinsic calibration method for laser line scanner operation in coordinate measuring machines

This article discusses different non contact 3D measuring strategies and presents a model for measuring complex geometry parts, manipulated through a robot arm, using a novel vision system consisting of a laser triangulation sensor and a motorized linear stage. First, the geometric model incorporating an automatic simple module for long term stability improvement will be outlined in the article. The new method used in the automatic module allows the sensor set up, including the motorized linear stage, for the scanning avoiding external measurement devices. In the measurement model the robot is just a positioning of parts with high repeatability. Its position and orientation data are not used for the measurement and therefore it is not directly “coupled” as an active component in the model. The function of the robot is to present the various surfaces of the workpiece along the measurement range of the vision system, which is responsible for the measurement. Thus, the whole system is not affected by the robot own errors following a trajectory, except those due to the lack of static repeatability. For the indirect link between the vision system and the robot, the original model developed needs only one first piece measuring as a “zero” or master piece, known by its accurate measurement using, for example, a Coordinate Measurement Machine. The strategy proposed presents a different approach to traditional laser triangulation systems on board the robot in order to improve the measurement accuracy, and several important cues for self-recalibration are explored using only a master piece. Experimental results are also presented to demonstrate the technique and the final 3D measurement accuracy.

https://www.mdpi.com/1424-8220/11/1/90/pdf

Juan José Aguilar

Papers

Kinematic parameter estimation technique for calibration and repeatability improvement of articulated arm coordinate measuring machines

Identification strategy of error parameter in volumetric error compensation of machine tool based on laser tracker measurements

Stereo vision for 3D measurement: accuracy analysis, calibration and industrial applications

A one-step intrinsic and extrinsic calibration method for laser line scanner operation in coordinate measuring machines

3D geometrical inspection of complex geometry parts using a novel laser triangulation sensor and a robot.