Preliminary results on algorithms for multi-kinect trajectory fusion in a living lab

Sevrin, Loic; Noury, Norbert; Abouchi, Nacer; Jumel, Fabrice; Massot, Bertrand; Saraydaryan, Jacques

doi:10.1016/j.irbm.2015.10.003

Cited by 11 publications

(9 citation statements)

References 11 publications

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“…We combine our work on autonomous navigation in crowded environments (human-aware navigation) [14] with service delivery strategies [3]. Human-Robot Interactions: We have been working for years on different interactions with robots (from teleoperation to multi-robot orchestration [15,16]). The Naoqi SDK, provided by Softbank Robotics with the Pepper robot, gives a set of API that are mainly used for Robot and Human interactions (speech recognition, text to speech, robot behavior feedbacks).…”

Section: Service Robots and Software Architecturesmentioning

confidence: 99%

Context Aware Robot Architecture, Application to the RoboCup@Home Challenge

Jumel

Saraydaryan

Leber

et al. 2019

RoboCup 2018: Robot World Cup XXII

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This paper presents an architecture dedicated to the orchestration of high level abilities of a humanoid robot, such as a Pepper, which must perform some tasks as the ones proposed in the RoboCup@Home competition. We present the main abilities that a humanoid service robot should provide. We choose to build them based on recent methodologies linked to social navigation and deep learning. We detail the architecture, on how high level abilities are connected with low level sub-functions. Finally we present first experimental results with a Pepper humanoid.

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Section: Service Robots and Software Architecturesmentioning

confidence: 99%

Context Aware Robot Architecture, Application to the RoboCup@Home Challenge

Jumel

Saraydaryan

Leber

et al. 2019

RoboCup 2018: Robot World Cup XXII

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“…However, these methods are prone to the errors caused by challenging illumination conditions in indoor environments. In addition, multiple Kinect sensors [38], [39] could be used, which nevertheless involve increased complexity in calibration and synchronisation [40], [41]. Different from these alternatives, in our work, the use of binaural and Kinect sensing offers the advantage in capturing the listener centred spatial audio scene, and the convenience in object localisation for metadata extraction from the scene.…”

Section: B Setup Justificationmentioning

confidence: 99%

Multiple Speaker Tracking in Spatial Audio via PHD Filtering and Depth-Audio Fusion

Liu

Wang

Campos

et al. 2018

IEEE Trans. Multimedia

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In object-based spatial audio system, positions of the audio objects (e.g. speakers/talkers or voices) presented in the sound scene are required as important metadata attributes for object acquisition and reproduction. Binaural microphones are often used as a physical device to mimic human hearing and to monitor and analyse the scene, including localisation and tracking of multiple speakers. The binaural audio tracker, however, is usually prone to the errors caused by room reverberation and background noise. To address this limitation, we present a multimodal tracking method by fusing the binaural audio with depth information (from a depth sensor, e.g., Kinect). More specifically, the PHD filtering framework is first applied to the depth stream, and a novel clutter intensity model is proposed to improve the robustness of the PHD filter when an object is occluded either by other objects or due to the limited field of view of the depth sensor. To compensate mis-detections in the depth stream, a novel gap filling technique is presented to map audio azimuths obtained from the binaural audio tracker to 3D positions, using speaker-dependent spatial constraints learned from the depth stream. With our proposed method, both the errors in the binaural tracker and the mis-detections in the depth tracker can be significantly reduced. Real-room recordings are used to show the improved performance of the proposed method in removing outliers and reducing mis-detections.

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“…Sevrin et al [15] proposed a people localisation system with a multiple Kinects trajectory fusion algorithm. The system adaptively selected the best possible choice among the Kinects in order to detect people with a highly accurate rate [16].…”

Section: Related Workmentioning

confidence: 99%

A 3D localisation method in indoor environments for virtual reality applications

Song

Liu

Tian

et al. 2017

Hum. Cent. Comput. Inf. Sci.

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Virtual Reality (VR) has recently experienced rapid development for human–computer interactions. Users wearing VR headsets gain an immersive experience when interacting with a 3-dimensional (3D) world. We utilise a light detection and ranging (LiDAR) sensor to detect a 3D point cloud from the real world. To match the scale between a virtual environment and a user’s real world, this paper develops a boundary wall detection method using the Hough transform algorithm. A connected-component-labelling (CCL) algorithm is applied to classify the Hough space into several distinguishable blocks that are segmented using a threshold. The four largest peaks among the segmented blocks are extracted as the parameters of the wall plane. The virtual environment is scaled to the size of the real environment. In order to synchronise the position of the user and his/her avatar in the virtual world, a wireless Kinect network is proposed for user localisation. Multiple Kinects are mounted in an indoor environment to sense the user’s information from different viewpoints. The proposed method supports the omnidirectional detection of the user’s position and gestures. To verify the performance of our proposed system, we developed a VR game using several Kinects and a Samsung Gear VR device.

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Preliminary results on algorithms for multi-kinect trajectory fusion in a living lab

Cited by 11 publications

References 11 publications

Context Aware Robot Architecture, Application to the RoboCup@Home Challenge

Context Aware Robot Architecture, Application to the RoboCup@Home Challenge

Multiple Speaker Tracking in Spatial Audio via PHD Filtering and Depth-Audio Fusion

A 3D localisation method in indoor environments for virtual reality applications

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