Mobiscan3D: A Low Cost Framework for Real Time Dense 3D Reconstruction on Mobile Devices

Bhowmick, Brojeshwar; Mallik, Apurbaa; Saha, Arindam

doi:10.1109/uic-atc-scalcom.2014.79

Cited by 10 publications

(6 citation statements)

References 16 publications

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“…Mostly, all state-of-the-art SLAM systems [12][13][14][15] and reconstruction methods using IMUs [16,17] rely on the pose-graph/ factorgraph optimization [18,19] or bundle adjustment. In the following section we will review the related work on object-SLAM and discuss some limitations in them and the keypoint based approach which motivated for the proposed approach.…”

Section: Related Workmentioning

confidence: 99%

Integrating Objects into Monocular SLAM

Joshi

Sharma

Parkhiya

et al. 2018

Proceedings of the 11th Indian Conference on Computer Vision, Graphics and Image Processing

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We propose a novel Line based parameterization for category specific CAD models. The proposed parameterization associates 3D category-specific CAD model and object under consideration using a dictionary based RANSAC method that uses object Viewpoints as prior and edges detected in the respective intensity image of the scene. The association problem is posed as a classical Geometry problem rather than being dataset driven, thus saving the time and labour that one invests in annotating dataset to train Keypoint Network[1, 2] for different category objects. Besides eliminating the need of dataset preparation, the approach also speeds up the entire process as this method processes the image only once for all objects, thus eliminating the need of invoking the network for every object in an image across all images. A 3D-2D edge association module followed by a resection algorithm for lines is used to recover object poses. The formulation optimizes for shape and pose of the object, thus aiding in recovering object 3D structure more accurately. Finally, a Factor Graph formulation is used to combine object poses with camera odometry to formulate a SLAM problem.

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Section: Related Workmentioning

confidence: 99%

Integrating Objects into Monocular SLAM

Joshi

Sharma

Parkhiya

et al. 2018

Proceedings of the 11th Indian Conference on Computer Vision, Graphics and Image Processing

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“…Recently, smart phones are used for image acquisition due to its low cost and easy availability. So researchers used smart phones sensors like accelerometer, magnetometer for data collection and 3D reconstruction, it reduces computation [11,12] and few works such as [13,14] have accomplished this, but the output is noisy due to a fast and course reconstruction. A system capable of dense 3D reconstruction of an unknown environment in real-time through a mobile robot requires simultaneous localization and mapping (SLAM) [15].…”

Section: Machine Visionmentioning

confidence: 99%

“…The captured information is pushed back to a backend server from where user is controlling the robot. Odometry and IMU sensor data are used for robot localization along with camera pose estimation [13]. Multi-view geometry [32] is used for creating a 3D map [11] of the environment.…”

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

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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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“…Having a depth of the scene computed from RGB [6] or using both RGB and IMU [7]. [8] may help but still associating the depth an d language is a challenge. Firstly, communicating the exact turn angle towards the goal only through natural language command is very difficult for any person, especially in cases where the robot may be oriented in any direction(towards the person in our case) hence predefined discrete actions may not be of much help in many of the indoor scenarios.…”

Section: Introductionmentioning

confidence: 99%

Sharing Cognition: Human Gesture and Natural Language Grounding Based Planning and Navigation for Indoor Robots

Kumar,

Maity,

Roychoudhury

et al. 2021

Preprint

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Cooperation among humans makes it easy to execute tasks and navigate seamlessly even in unknown scenarios. With our individual knowledge and collective cognition skills, we can reason about and perform well in unforeseen situations and environments. To achieve a similar potential for a robot navigating among humans and interacting with them, it is crucial for it to acquire the ability for easy, efficient and natural ways of communication and cognition sharing with humans. In this work, we aim to exploit human gestures which is known to be the most prominent modality of communication after the speech. We demonstrate how the incorporation of gestures for communicating spatial understanding can be achieved in a very simple yet effective way using a robot having the vision and listening capability. This shows a big advantage over using only Vision and Language-based Navigation, Language Grounding or Human-Robot Interaction in a task requiring the development of cognition and indoor navigation. We adapt the state-of-the-art modules of Language Grounding and Human-Robot Interaction to demonstrate a novel system pipeline in real-world environments on a Telepresence robot for performing a set of challenging tasks. To the best of our knowledge, this is the first pipeline to couple the fields of HRI and language grounding in an indoor environment to demonstrate autonomous navigation.

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Mobiscan3D: A Low Cost Framework for Real Time Dense 3D Reconstruction on Mobile Devices

Cited by 10 publications

References 16 publications

Integrating Objects into Monocular SLAM

Integrating Objects into Monocular SLAM

Human-Like Sensing for Robotic Remote Inspection and Analytics

Sharing Cognition: Human Gesture and Natural Language Grounding Based Planning and Navigation for Indoor Robots

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