scholarly journals A Real-Time 3D Perception and Reconstruction System Based on a 2D Laser Scanner

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Zheng Fang ◽  
Shibo Zhao ◽  
Shiguang Wen ◽  
Yu Zhang
Keyword(s):  
Real Time ◽  
Point Cloud ◽  
Low Cost ◽  
Laser Scanner ◽  
Continuous Mapping ◽  
Step Motor ◽  
3D Point Cloud ◽  
3D Perception ◽  
Mapping System ◽  
Local Point

This paper presents a real-time and low-cost 3D perception and reconstruction system which is suitable for autonomous navigation and large-scale environment reconstruction. The 3D mapping system is based on a rotating 2D planar laser scanner driven by a step motor, which is suitable for continuous mapping. However, for such a continuous mapping system, the challenge is that the range measurements are received at different times when the 3D LiDAR is moving, which will result in big distortion of the local 3D point cloud. As a result, the errors in motion estimation can cause misregistration of the resulting point cloud. In order to continuously estimate the trajectory of the sensor, we first extract feature points from the local point cloud and then estimate the transformation between current frame to local map to get the LiDAR odometry. After that, we use the estimated motion to remove the distortion of the local point cloud and then register the undistorted local point cloud to the global point cloud to get accurate global map. Finally, we propose a coarse-to-fine graph optimization method to minimize the global drift. The proposed 3D sensor system is advantageous due to its mechanical simplicity, mobility, low weight, low cost, and real-time estimation. To validate the performance of the proposed system, we carried out several experiments to verify its accuracy, robustness, and efficiency. The experimental results show that our system can accurately estimate the trajectory of the sensor and build a quality 3D point cloud map simultaneously.

scholarly journals UAV-BASED ACQUISITION OF 3D POINT CLOUD – A COMPARISON OF A LOW-COST LASER SCANNER AND SFM-TOOLS

2015 ◽  
Vol XL-3/W3 ◽  
pp. 335-341 ◽  
Author(s):  
D. Mader ◽  
R. Blaskow ◽  
P. Westfeld ◽  
H.-G. Maas
Keyword(s):  
Structure From Motion ◽  
Point Cloud ◽  
Laser Scanning ◽  
Near Infrared ◽  
Low Cost ◽  
Laser Scanner ◽  
Infrared Camera ◽  
Image Data ◽  
3D Point Cloud ◽  
Efficient System

The Project ADFEX (Adaptive Federative 3D Exploration of Multi Robot System) pursues the goal to develop a time- and cost-efficient system for exploration and monitoring task of unknown areas or buildings. A fleet of unmanned aerial vehicles equipped with appropriate sensors (laser scanner, RGB camera, near infrared camera, thermal camera) were designed and built. A typical operational scenario may include the exploration of the object or area of investigation by an UAV equipped with a laser scanning range finder to generate a rough point cloud in real time to provide an overview of the object on a ground station as well as an obstacle map. The data about the object enables the path planning for the robot fleet. Subsequently, the object will be captured by a RGB camera mounted on the second flying robot for the generation of a dense and accurate 3D point cloud by using of structure from motion techniques. In addition, the detailed image data serves as basis for a visual damage detection on the investigated building. <br><br> This paper focuses on our experience with use of a low-cost light-weight Hokuyo laser scanner onboard an UAV. The hardware components for laser scanner based 3D point cloud acquisition are discussed, problems are demonstrated and analyzed, and a quantitative analysis of the accuracy potential is shown as well as in comparison with structure from motion-tools presented.

scholarly journals Low-Cost Calibration of Matching Error between Lidar and Motor for a Rotating 2D Lidar

2021 ◽  
Vol 11 (3) ◽  
pp. 913
Author(s):  
Chang Yuan ◽  
Shusheng Bi ◽  
Jun Cheng ◽  
Dongsheng Yang ◽  
Wei Wang
Keyword(s):  
Point Cloud ◽  
Low Cost ◽  
Shape Error ◽  
3D Point Cloud ◽  
Motor Shaft ◽  
Matching Error ◽  
Triangular Plate ◽  
Attitude Error ◽  
Shaft Angle ◽  
The Cost

For a rotating 2D lidar, the inaccurate matching between the 2D lidar and the motor is an important error resource of the 3D point cloud, where the error is shown both in shape and attitude. Existing methods need to measure the angle position of the motor shaft in real time to synchronize the 2D lidar data and the motor shaft angle. However, the sensor used for measurement is usually expensive, which can increase the cost. Therefore, we propose a low-cost method to calibrate the matching error between the 2D lidar and the motor, without using an angular sensor. First, the sequence between the motor and the 2D lidar is optimized to eliminate the shape error of the 3D point cloud. Next, we eliminate the attitude error with uncertainty of the 3D point cloud by installing a triangular plate on the prototype. Finally, the Levenberg–Marquardt method is used to calibrate the installation error of the triangular plate. Experiments verified that the accuracy of our method can meet the requirements of the 3D mapping of indoor autonomous mobile robots. While we use a 2D lidar Hokuyo UST-10LX with an accuracy of ±40 mm in our prototype, we can limit the mapping error within ±50 mm when the distance is no more than 2.2996 m for a 1 s scan (mode 1), and we can limit the mapping error within ±50 mm at the measuring range 10 m for a 16 s scan (mode 7). Our method can reduce the cost while the accuracy is ensured, which can make a rotating 2D lidar cheaper.

Real-Time 3D object detection using improved convolutional neural network based on image-driven point cloud

2021 ◽  
Vol 14 ◽  
Author(s):  
Zhiyong Gao ◽  
Jianhong Xiang
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Point Cloud ◽  
Point Clouds ◽  
3D Point Cloud ◽  
3D Object ◽  
3D Object Detection ◽  
Instance Segmentation

Background: While detecting the object directly from the 3D point cloud, the natural 3D patterns and invariance of 3D data are often obscure. Objective: In this work, we aimed at studying the 3D object detection from discrete, disordered and sparse 3D point clouds. Methods: The CNN is composed of the frustum sequence module, 3D instance segmentation module S-NET, 3D point cloud transformation module T-NET, and 3D boundary box estimation module E-NET. The search space of the object is determined by the frustum sequence module. The instance segmentation of the point cloud is performed by the 3D instance segmentation module. The 3D coordinates of the object are confirmed by the transformation module and the 3D bounding box estimation module. Results: Evaluated on KITTI benchmark dataset, our method outperforms the state of the art by remarkable margins while having real-time capability. Conclusion: We achieve real-time 3D object detection by proposing an improved convolutional neural network (CNN) based on image-driven point clouds.

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

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.

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