3D point cloud generation from 2D depth camera images using successive triangulation

Pal, Bishwajit; Khaiyum, Samitha; Kumaraswamy, Y. S.

doi:10.1109/icimia.2017.7975586

Cited by 9 publications

(4 citation statements)

References 8 publications

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“…The RTAB-Map receives this data, generates the map data and further publishes it to rtabmapviz for final visualisation. This is illustrated in Figure [2].…”

Section: Ros Implementationmentioning

confidence: 93%

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Dynamic Object Elimination based Indoor 3D Mapping System

Mane¹

2021

IJRASET

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In today's world, the need for autonomous robots is increasing at an exponential rate and the implementation of Simultaneous Localisation And Mapping (SLAM) is gaining more and more attention. One of the major component of SLAM is 3D Mapping of the environment which enables autonomous robots to perceive the environment like a human does for which many Depth cameras or RGB-D cameras prove useful. This paper proposes a continuous real-time 3D mapping system that tackles the long existing problem of point cloud distortion induced by dynamic objects in the frame. Our method uses the Microsoft Kinect V1 as the RGB-D camera and the packages in the Robotic Operating System (ROS) like the Real Time Appearance Based Mapping (RTAB-map) for 3D reconstruction. A ROS based method is used to implement dynamic object elimination in real-time. For the purpose of dynamic objects detection in the frame, two algorithms -Deep Learning based tiny YOLO-v3 and a Machine Learning based Haar Cascade classifier are used. The results from the two are compared in terms of accuracy, execution time and mean Average Precision (mAP) and it was inferred that although HaarCascade model is comparatively less accurate when detecting objects, it is two times faster than YOLO which makes the system more real-time. The real-time implementation was given more preference while selecting the model.

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“…The RTAB-Map receives this data, generates the map data and further publishes it to rtabmapviz for final visualisation. This is illustrated in Figure [2].…”

Section: Ros Implementationmentioning

confidence: 93%

“…Another traditional method is Depth Estimation using Stereo Vision which works on the principal of triangulation [2,3]. Since the images are obtained from 2 different cameras in a stereo system, feature matching algorithms are needed in order to synchronize the two cameras in the stereo system [4].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Object Elimination based Indoor 3D Mapping System

Mane¹

2021

IJRASET

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“…Pal [36] uses the continuous triangulation method to generate a three-dimensional point cloud of the stereo camera. Wei [37] obtains the three-dimensional information of apple tree obstacles through a binocular stereo camera.…”

Section: Stereo Cameramentioning

confidence: 99%

An Overview of the Application of Machine Vision in Recognition and Localization of Fruit and Vegetable Harvesting Robots

Hou,

Chen,

Jiang

et al. 2023

Agriculture

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Intelligent agriculture imposes higher requirements on the recognition and localization of fruit and vegetable picking robots. Due to its unique visual information and relatively low hardware cost, machine vision is widely applied in the recognition and localization of fruit and vegetable picking robots. This article provides an overview of the application of machine vision in the recognition and localization of fruit and vegetable picking robots. Firstly, the advantages, disadvantages, and the roles of different visual sensors and machine vision algorithms in the recognition and localization of fruit and vegetable picking robots are introduced, including monocular cameras, stereo cameras, structured light cameras, multispectral cameras, image segmentation algorithms, object detection algorithms, and 3D reconstruction algorithms. Then, the current status and challenges faced by machine vision in the recognition and localization of fruit and vegetable picking robots are summarized. These challenges include the stability of fast recognition under complex background interference, stability of recognition under different lighting environments for the same crop, the reliance of recognition and localization on prior information in the presence of fruit overlap and occlusions caused by leaves and branches, and the uncertainty of picking caused by complex working environments. In current research on algorithms dealing with complex background interference and various occlusion disturbances, good results have been achieved. Different lighting environments have a significant impact on the recognition and positioning of fruits and vegetables, with a minimum accuracy of 59.2%. Finally, this article outlines future research directions to address these challenges.

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“…LiDAR is available in both two-dimensional (2D) and three-dimensional (3D) versions [17], [18]. The 2D LiDAR generates a point cloud consisting of x and y coordinates, which represent the locations of points on a 2D plane [19].…”

Section: Introductionmentioning

confidence: 99%

Utilizing a Single-Stage 2D Detector in 3D LiDAR Point Cloud With Vertical Cylindrical Coordinate Projection for Human Identification

Eka Budiyanta,

Mulyanto Yuniarno,

Usagawa

et al. 2024

IEEE Access

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Exploiting sensitive human data in human visual monitoring system violates individual's privacy. Hence, this study utilized a Light Detection and Ranging (LiDAR)-generated three-dimensional (3D) point cloud instead of an RGB camera as it captures the human object without detailed imagery. Given their dispersed nature, processing 3D LiDAR point cloud is economically inefficient as it requires some preliminary actions. Alternatively, this study applied a single-stage detection process using only one data type, namely 3D LiDAR point cloud with vertical axis direct projection. This approach utilized the 3D LiDAR cylindrical coordinates features to project the data onto two-dimensional (2D) image space with various computational capabilities and back-projection algorithm to restore the identified object on the 2D plane onto 3D LiDAR point cloud coordinates. This model also implemented the YOLOv5 series due to their varied sizes. The evaluation of this approach utilized accuracy metric which was the average of mean average precision (mAP) value based on different intersections over union (IoU) thresholds. The proposed methodology effectively employed a vertical projection technique to identify human objects. Notably, this approach distinguishes itself from previous methods such as PIXOR, BirdNet, BirdNet+, BevDetNet, and Frustum-PointPillars, offering a novel perspective in the field. In addition, the best and worst performing models had the accuracy values of 44.35% and 79.83%, with inference speeds of 3.7 ms and up to 25 ms, respectively. Further, the inference speeds of all models were less than 33.33 ms. Thus, the monitored objects were identified before the LiDAR system enters the next azimuth rotation.

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3D point cloud generation from 2D depth camera images using successive triangulation

Cited by 9 publications

References 8 publications

Dynamic Object Elimination based Indoor 3D Mapping System

Dynamic Object Elimination based Indoor 3D Mapping System

An Overview of the Application of Machine Vision in Recognition and Localization of Fruit and Vegetable Harvesting Robots

Utilizing a Single-Stage 2D Detector in 3D LiDAR Point Cloud With Vertical Cylindrical Coordinate Projection for Human Identification

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