Optimized 3D laser point cloud reconstruction by gradient descent technique

2020 ◽  
Vol 47 (3) ◽  
pp. 409-421
Author(s):  
Ravinder Singh ◽  
Archana Khurana ◽  
Sunil Kumar
Keyword(s):  
Point Cloud ◽  
Time Synchronization ◽  
Laser Scanner ◽  
Mobile Robot Navigation ◽  
Measurement Unit ◽  
3D Point Cloud ◽  
Servo Motor ◽  
Position Information ◽  
Content Type ◽  
Aerial Navigation

Purpose This study aims to develop an optimized 3D laser point reconstruction using Descent Gradient algorithm. Precise and accurate reconstruction of 3D laser point cloud of the complex environment/object is a key solution for many industries such as construction, gaming, automobiles, aerial navigation, architecture and automation. A 2D laser scanner along with a servo motor/pan tilt/inertial measurement unit is used for generating 3D point cloud (either environment/object or both) by acquiring the real-time data from sensors. However, while generating the 3D laser point cloud, various problems related to time synchronization problem between laser and servomotor and torque variation in servomotors arise, which causes misalignment in stacking the 2D laser scan for generating the 3D point cloud of the environment. Because of the misalignment in stacking, the 2D laser scan corresponding to the erroneous angular and position information by the servomotor and the 3D laser point cloud become distorted in terms of inconsistency for measuring the dimension of the objects. Design/methodology/approach This paper addresses a modified 3D laser system assembled from a 2D laser scanner coupled with a servomotor (dynamixel motor) for developing an efficient 3D laser point cloud with the implementation of an optimization technique: descent gradient filter (DGT). The proposed approach reduces the cost function (error) in the angular and position coordinates of the servo motor caused because of torque variation and time synchronization, which resulted in enhancing the accuracy in 3D point cloud mapping for the accurate measurement of the object’s dimensions. Findings Various real-world experiments are performed with the proposed DGT filter linked with laser scanner and servomotor and an improvement of 6.5 per cent in measuring the accurate dimension of object is obtained while comparing with conventional approaches for generating a 3D laser point cloud. Originality/value This proposed technique may be applicable for various industrial applications that are based on robotics arms (such as painting, welding and cutting) in the automobile industry, the optimized measurement of object, efficient mobile robot navigation, precise 3D reconstruction of environment/object in construction, architecture applications, airborne applications and aerial navigation.

Comparison of Camera and Laser Scanner based 3D Point Cloud

2019 ◽  
Author(s):  
Yawar Rehman ◽  
Hafiz M. Ameem Uddin ◽  
Taha Hasan Masood Siddique ◽  
Haris ◽  
Syed Riaz Un Nabi Jafri ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanner ◽  
3D Point Cloud

2D lidar to kinematic chain calibration using planar features of indoor scenes

2020 ◽  
Vol 47 (5) ◽  
pp. 647-655
Author(s):  
Bernardo Lourenço ◽  
Tiago Madeira ◽  
Paulo Dias ◽  
Vitor M. Ferreira Santos ◽  
Miguel Oliveira
Keyword(s):  
Point Cloud ◽  
Laser Scanner ◽  
Kinematic Chain ◽  
Point Clouds ◽  
Calibration Procedure ◽  
High Angular Resolution ◽  
Reconstruction Procedure ◽  
Content Type ◽  
Additional Degree ◽  
Indoor Scenes

Purpose 2D laser rangefinders (LRFs) are commonly used sensors in the field of robotics, as they provide accurate range measurements with high angular resolution. These sensors can be coupled with mechanical units which, by granting an additional degree of freedom to the movement of the LRF, enable the 3D perception of a scene. To be successful, this reconstruction procedure requires to evaluate with high accuracy the extrinsic transformation between the LRF and the motorized system. Design/methodology/approach In this work, a calibration procedure is proposed to evaluate this transformation. The method does not require a predefined marker (commonly used despite its numerous disadvantages), as it uses planar features in the point acquired clouds. Findings Qualitative inspections show that the proposed method reduces artifacts significantly, which typically appear in point clouds because of inaccurate calibrations. Furthermore, quantitative results and comparisons with a high-resolution 3D scanner demonstrate that the calibrated point cloud represents the geometries present in the scene with much higher accuracy than with the un-calibrated point cloud. Practical implications The last key point of this work is the comparison of two laser scanners: the lemonbot (authors’) and a commercial FARO scanner. Despite being almost ten times cheaper, the laser scanner was able to achieve similar results in terms of geometric accuracy. Originality/value This work describes a novel calibration technique that is easy to implement and is able to achieve accurate results. One of its key features is the use of planes to calibrate the extrinsic transformation.

scholarly journals ROAD SIGNS DETECTION AND RECOGNITION UTILIZING IMAGES AND 3D POINT CLOUD ACQUIRED BY MOBILE MAPPING SYSTEM

2016 ◽  
Vol XLI-B1 ◽  
pp. 669-673
Author(s):  
Y. H. Li ◽  
T. Shinohara ◽  
T. Satoh ◽  
K. Tachibana
Keyword(s):  
Template Matching ◽  
Point Cloud ◽  
3D Point Cloud ◽  
Mobile Mapping ◽  
Position Information ◽  
Road Signs ◽  
Road Sign ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Detection And Recognition

High-definition and highly accurate road maps are necessary for the realization of automated driving, and road signs are among the most important element in the road map. Therefore, a technique is necessary which can acquire information about all kinds of road signs automatically and efficiently. Due to the continuous technical advancement of Mobile Mapping System (MMS), it has become possible to acquire large number of images and 3d point cloud efficiently with highly precise position information. In this paper, we present an automatic road sign detection and recognition approach utilizing both images and 3D point cloud acquired by MMS. The proposed approach consists of three stages: 1) detection of road signs from images based on their color and shape features using object based image analysis method, 2) filtering out of over detected candidates utilizing size and position information estimated from 3D point cloud, region of candidates and camera information, and 3) road sign recognition using template matching method after shape normalization. The effectiveness of proposed approach was evaluated by testing dataset, acquired from more than 180 km of different types of roads in Japan. The results show a very high success in detection and recognition of road signs, even under the challenging conditions such as discoloration, deformation and in spite of partial occlusions.

scholarly journals Accurate Calibration of a Self-Developed Vehicle-Borne LiDAR Scanning System

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Ming Guo ◽  
Bingnan Yan ◽  
Tengfei Zhou ◽  
Deng Pan ◽  
Guoli Wang
Keyword(s):  
Point Cloud ◽  
Laser Scanner ◽  
Measurement Data ◽  
Point Clouds ◽  
Calibration Method ◽  
Linear Interpolation ◽  
Measurement Unit ◽  
Scanning System ◽  
High Precision Measurement ◽  
Distance Ratio

To obtain high-precision measurement data using vehicle-borne light detection and ranging (LiDAR) scanning (VLS) systems, calibration is necessary before a data acquisition mission. Thus, a novel calibration method based on a homemade target ball is proposed to estimate the system mounting parameters, which refer to the rotational and translational offsets between the LiDAR sensor and inertial measurement unit (IMU) orientation and position. Firstly, the spherical point cloud is fitted into a sphere to extract the coordinates of the centre, and each scan line on the sphere is fitted into a section of the sphere to calculate the distance ratio from the centre to the nearest two sections, and the attitude and trajectory parameters of the centre are calculated by linear interpolation. Then, the real coordinates of the centre of the sphere are calculated by measuring the coordinates of the reflector directly above the target ball with the total station. Finally, three rotation parameters and three translation parameters are calculated by two least-squares adjustments. Comparisons of the point cloud before and after calibration and the calibrated point clouds with the point cloud obtained by the terrestrial laser scanner show that the accuracy significantly improved after calibration. The experiment indicates that the calibration method based on the homemade target ball can effectively improve the accuracy of the point cloud, which can promote VLS development and applications.

scholarly journals HIGH ACCURATE POINTWISE (GEO-)REFERENCING OF A K-TLS BASED MULTI-SENSOR-SYSTEM

2018 ◽  
Vol IV-4 ◽  
pp. 81-88 ◽  
Author(s):  
J. Hartmann ◽  
P. Trusheim ◽  
H. Alkhatib ◽  
J.-A. Paffenholz ◽  
D. Diener ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Point Cloud ◽  
Laser Scanning ◽  
Laser Scanner ◽  
High Accuracy ◽  
Laser Tracker ◽  
3D Point Cloud ◽  
Filter Model ◽  
Measurement Frequency ◽  
Set Up

<p><strong>Abstract.</strong> In recent years, the requirements in the industrial production, e.g., ships or planes, have been increased. In addition to high accuracy requirements with a standard deviation of 1<span class="thinspace"></span>mm, an efficient 3D object capturing is required. In terms of efficiency, kinematic laser scanning (k-TLS) has been proven its worth in recent years. It can be seen as an alternative to the well established static terrestrial laser scanning (s-TLS). However, current k-TLS based multi-sensor-systems (MSS) are not able to fulfil the high accuracy requirements. Thus, a new k-TLS based MSS and suitable processing algorithms have to be developed. In this contribution a new k-TLS based MSS will be presented. The main focus will lie on the (geo-)referencing process. Due to the high accuracy requirements, a novel procedure of external (geo-)referencing is used here. Hereby, a mobile platform, which is equipped with a profile laser scanner, will be tracked by a laser tracker. Due to the fact that the measurement frequency of the laser scanner is significantly higher than the measurement frequency of the laser tracker a direct point wise (geo-)referencing is not possible. To enable this a Kalman filter model is set up and implemented. In the prediction step each point is shifted according to the determined velocity of the platform. Because of the nonlinear motion of the platform an iterative extended Kalman filter (iEKF) is used here. Furthermore, test measurements of a panel with the k-TLS based MSS and with s-TLS were carried out. To compare the results, the 3D distances with the M3C2-algorithm between the s-TLS 3D point cloud and the k-TLS 3D point cloud are estimated. It can be noted, that the usage of a system model for the (geo-)referencing is essential. The results show that the mentioned high accuracy requirements have been achieved.</p>

scholarly journals Co-Segmentation and Superpixel-Based Graph Cuts for Building Change Detection from Bi-Temporal Digital Surface Models and Aerial Images

2019 ◽  
Vol 11 (6) ◽  
pp. 729 ◽  
Author(s):  
Shiyan Pang ◽  
Xiangyun Hu ◽  
Mi Zhang ◽  
Zhongliang Cai ◽  
Fengzhu Liu
Keyword(s):  
Change Detection ◽  
Point Cloud ◽  
Laser Scanner ◽  
Graph Cuts ◽  
Aerial Images ◽  
Processing Unit ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Surface Models

Thanks to the recent development of laser scanner hardware and the technology of dense image matching (DIM), the acquisition of three-dimensional (3D) point cloud data has become increasingly convenient. However, how to effectively combine 3D point cloud data and images to realize accurate building change detection is still a hotspot in the field of photogrammetry and remote sensing. Therefore, with the bi-temporal aerial images and point cloud data obtained by airborne laser scanner (ALS) or DIM as the data source, a novel building change detection method combining co-segmentation and superpixel-based graph cuts is proposed in this paper. In this method, the bi-temporal point cloud data are firstly combined to achieve a co-segmentation to obtain bi-temporal superpixels with the simple linear iterative clustering (SLIC) algorithm. Secondly, for each period of aerial images, semantic segmentation based on a deep convolutional neural network is used to extract building areas, and this is the basis for subsequent superpixel feature extraction. Again, with the bi-temporal superpixel as the processing unit, a graph-cuts-based building change detection algorithm is proposed to extract the changed buildings. In this step, the building change detection problem is modeled as two binary classifications, and acquisition of each period’s changed buildings is a binary classification, in which the changed building is regarded as foreground and the other area as background. Then, the graph cuts algorithm is used to obtain the optimal solution. Next, by combining the bi-temporal changed buildings and digital surface models (DSMs), these changed buildings are further classified as “newly built,” “taller,” “demolished”, and “lower”. Finally, two typical datasets composed of bi-temporal aerial images and point cloud data obtained by ALS or DIM are used to validate the proposed method, and the experiments demonstrate the effectiveness and generality of the proposed algorithm.

scholarly journals Towards Efficient Implementation of an Octree for a Large 3D Point Cloud

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4398 ◽  
Author(s):  
Soohee Han
Keyword(s):  
Point Cloud ◽  
Laser Scanner ◽  
Point Clouds ◽  
Main Memory ◽  
3D Point Cloud ◽  
Airborne Laser Scanner ◽  
Memory Efficiency ◽  
3D Point Clouds ◽  
Short Tunnel ◽  
Better Than

The present study introduces an efficient algorithm to construct a file-based octree for a large 3D point cloud. However, the algorithm was very slow compared with a memory-based approach, and got even worse when using a 3D point cloud scanned in longish objects like tunnels and corridors. The defects were addressed by implementing a semi-isometric octree group. The approach implements several semi-isometric octrees in a group, which tightly covers the 3D point cloud, though each octree along with its leaf node still maintains an isometric shape. The proposed approach was tested using three 3D point clouds captured in a long tunnel and a short tunnel by a terrestrial laser scanner, and in an urban area by an airborne laser scanner. The experimental results showed that the performance of the semi-isometric approach was not worse than a memory-based approach, and quite a lot better than a file-based one. Thus, it was proven that the proposed semi-isometric approach achieves a good balance between query performance and memory efficiency. In conclusion, if given enough main memory and using a moderately sized 3D point cloud, a memory-based approach is preferable. When the 3D point cloud is larger than the main memory, a file-based approach seems to be the inevitable choice, however, the semi-isometric approach is the better option.

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