3D Environment Reconstruction Using Mobile Robot Platform and Monocular Vision

In this paper we introduce Human-like Sensing or ''5 senses computing'' as a natural futuristic extension of Internet-of-Things. We discuss how 3D optical vision, thermal vision, acoustic profiling, olfaction and tactile sensing can help in remote inspection and analytics solutions. We propose a robot mounted opto-thermal and acoustic sensing system as a possible integrated system to gather such data. We present results of field experiments conducted with the proposed system and show how such systems can provide acceptable solutions for remote inspection and analytics.

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“…Multi-view geometry [32] is used for creating a 3D map [11] of the environment. The details of the entire process are explained in [33].…”

Section: Proposed Integrated Sensing Systemmentioning

confidence: 99%

Human-Like Sensing for Robotic Remote Inspection and Analytics

Pal

Dasgupta

Saha

et al. 2016

Wireless Pers Commun

Self Cite

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“…In recent years, various improvements in SfM have been proposed to increase the robustness and accuracy. In [23], a framework of 3D reconstruction on an inexpensive robotic platform using a webcam and robot wheel odometry is proposed. The results show that the technique is efficient and robust to a variety of environment scenarios with different scales and sizes.…”

Section: Features-based Registrationmentioning

confidence: 99%

Indoor Environment Reconstruction for Unmanned System Using Multiple Low-Cost Sensors

Wang,

Ding,

Wang

et al. 2024

Remote Sensing

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Considering that laser scan stations are expensive and heavy for the indoor environment reconstruction of unmanned systems, a low-cost and light reconstruction system is proposed in this research. The system consists of a cross-structured light visual (SLV) sensor and an inertial navigation system (INS). The cross-SLV sensor is used to scan the surroundings and to estimate the angle change between two adjacent measurements. To improve the robustness and accuracy of the angle measurement, a Kalman Filter (KF) with simple construction is designed to fuse the inertial information from the INS. The factors which influence ranging accuracy are analyzed and ranging experiments show that the SLV sensor has an accuracy of higher than 2% when the distance is less than 4 m. Then the reconstruction results of a kitchen and corridor show that the error of most points is less than 50 mm for either kitchen (94%) or corridor (85%), and the mean errors and standard deviations of kitchen and corridor are less than 20 mm and 30 mm, respectively.

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“…The 2D range finder sensor is a common type used to scan the obstacles in the environments. It performs a more accurate map than a monocular camera or other vision sensors, such as [33]. Although there are a number of researchers using time-of-flight (ToF) camera to capture depth image of the environment for map building like [88,161,77], a 2D range finder sensor is much more economical than a ToF camera and has a larger scanning angle.…”

Section: Safe Area Search and Map Building Algorithmsmentioning

confidence: 99%

“…In Chapter 6, we present an safe area search and 3D map building algorithm for ground mobile robot in 3D environment. Comparing with the previous works, the main feature of our method is that it combines the complete 3D map building and the collision-free area search navigation together in one method, unlike other works [161,16,77,33]. Furthermore, we consider a 2D range finder sensor to scan the 3D structure of the environment.…”

Section: Contributionsmentioning

confidence: 99%

Sensor Network Based Collision-Free Navigation and Map Building for Mobile Robots

2017

Preprint

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Safe robot navigation is a fundamental research field for autonomous robots including ground mobile robots and flying robots. The primary objective of a safe robot navigation algorithm is to guide an autonomous robot from its initial position to a target or along a desired path with obstacle avoidance. With the development of information technology and sensor technology, the implementations combining robotics with sensor network are focused on in the recent researches. One of the relevant implementations is the sensor network based robot navigation. Moreover, another important navigation problem of robotics is safe area search and map building.In this report, a global collision-free path planning algorithm for ground mobile robots in dynamic environments is presented firstly. Considering the advantages of sensor network, the presented path planning algorithm is developed to a sensor network based navigation algorithm for ground mobile robots. The 2D range finder sensor network is used in the presented method to detect static and dynamic obstacles. The sensor network can guide each ground mobile robot in the detected safe area to the target. The computer simulations and experiments confirm the performance of the presented method. Furthermore, considering the implementations of small-sized flying robots in industry, the presented navigation algorithm is extended into 3D environments. In the presented method, a time-of-flight camera network is used to detect the static and moving obstacles. With the measurements of the sensor network, any flying robot in the workspace is navigated by the presented algorithm from the initial position to the target and avoids any obstacles in the workspace.Moreover, in this report, another navigation problem, safe area search and map building for ground mobile robot, is studied and two algorithms are presented. In the first presented method, we consider a ground mobile robot equipped with a 2D range finder sensor searching a bounded 2D area without any collision and building a complete 2D map of the area. Furthermore, the first presented map building algorithm is extended to another algorithm for 3D map building.

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3D Environment Reconstruction Using Mobile Robot Platform and Monocular Vision

Cited by 7 publications

References 17 publications

Human-Like Sensing for Robotic Remote Inspection and Analytics

Human-Like Sensing for Robotic Remote Inspection and Analytics

Indoor Environment Reconstruction for Unmanned System Using Multiple Low-Cost Sensors

Sensor Network Based Collision-Free Navigation and Map Building for Mobile Robots

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