This paper addresses the challenge of humanoid robot teleoperation in a natural indoor environment via a Brain-Computer Interface (BCI). We leverage deep Convolutional Neural Network (CNN) based image and signal understanding to facilitate both real-time object detection and dry-Electroencephalography (EEG) based human cortical brain bio-signals decoding. We employ recent advances in dry-EEG technology to stream and collect the cortical waveforms from subjects while they fixate on variable Steady State Visual Evoked Potential (SSVEP) stimuli generated directly from the environment the robot is navigating. To these ends, we propose the use of novel variable BCI stimuli by utilising the real-time video streamed via the on-board robot camera as visual input for SSVEP, where the CNN detected natural scene objects are altered and flickered with differing frequencies (10Hz, 12Hz and 15Hz). These stimuli are not akin to traditional stimuli - as both the dimensions of the flicker regions and their on-screen position changes depending on the scene objects detected. Onscreen object selection via such a dry-EEG enabled SSVEP methodology, facilitates the on-line decoding of human cortical brain signals, via a specialised secondary CNN, directly into teleoperation robot commands (approach object, move in a specific direction: right, left or back). This SSVEP decoding model is trained via a priori offline experimental data in which very similar visual input is present for all subjects. The resulting classification demonstrates high performance with mean accuracy of 85 for the real-time robot navigation experiment across multiple test subjects.
There are two notable studies that have integrated object detection and recognition @cite_29 @cite_1 . In @cite_29 , the authors used seven different frequencies to navigate a mobile robot to a storage rack to grasp an object and delivered into a dustbin with an average mean accuracy of 89.4 The authors in @cite_25 used SSVEP with hybrid-mask feature in which a 3D textured model were rendered and flickered on certain scene objects. In this case, three similar cans which are recognised offline. Subjects for this study teleoperated a humanoid robot HRP-2 (located in Japan from Italy) to control the robot to grasp a can from a table and navigate the robot to a second table where the robot need to drop the can on a marked target.
@cite_29 @cite_1 @cite_25
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We present an unsupervised framework for simultaneous appearance-based object discovery, detection, tracking and reconstruction using RGBD cameras and a robot manipulator. The system performs dense 3D simultaneous localization and mapping concurrently with unsupervised object discovery. Putative objects that are spatially and visually coherent are manipulated by the robot to gain additional motion-cues. The robot uses appearance alone, followed by structure and motion cues, to jointly discover, verify, learn and improve models of objects. Induced motion segmentation reinforces learned models which are represented implicitly as 2D and 3D level sets to capture both shape and appearance. We compare three different approaches for appearance-based object discovery and find that a novel form of spatio-temporal super-pixels gives the highest quality candidate object models in terms of precision and recall. Live experiments with a Baxter robot demonstrate a holistic pipeline capable of automatic discovery, verification, detection, tracking and reconstruction of unknown objects. Despite the rich information provided by sensors such as the Microsoft Kinect in the robotic perception setting, the problem of detecting object instances remains unsolved, even in the tabletop setting, where segmentation is greatly simplified. Existing object detection systems often focus on textured objects, for which local feature descriptors can be used to reliably obtain correspondences between different views of the same object. We examine the benefits of dense feature extraction and multimodal features for improving the accuracy and robustness of an instance recognition system. By combining multiple modalities and blending their scores through an ensemble-based method in order to generate our final object hypotheses, we obtain significant improvements over previously published results on two RGB-D datasets. On the Challenge dataset, our method results in only one missed detection (achieving 100 precision and 99.77 recall). On the Willow dataset, we also make significant gains on the prior state of the art (achieving 98.28 precision and 87.78 recall), resulting in an increase in F-score from 0.8092 to 0.9273. Temporal object detection has attracted significant attention, but most popular detection methods can not leverage the rich temporal information in video or robotic vision. Although many different algorithms have been developed for video detection task, real-time online approaches are frequently deficient. In this paper, based on attention mechanism and convolutional long short-term memory (ConvLSTM), we propose a temporal single-shot detector (TSSD) for robotic vision. Distinct from previous methods, we take aim at temporally integrating pyramidal feature hierarchy using ConvLSTM, and design a novel structure including a high-level temporal unit as well as a low-level one (HL-TU) for multi-scale feature maps. Moreover, we develop a creative temporal analysis unit, namely, attention-aware ConvLSTM (AC-LSTM), in which a temporal attention module is specially tailored for background suppression and scale suppression while ConvLSTM temporally integrates attention-aware features. An association loss is designed for temporal coherence. Finally, our method is evaluated on ImageNet VID dataset. Extensive comparisons on the detection capability confirm or validate the superiority of the proposed approach. Consequently, the developed TSSD is fairly faster and achieves an overall competitive performance in terms of mean average precision. As a temporal, real-time, and online detector, TSSD is applicable to robot's intelligent perception. Detection of objects in cluttered indoor environments is one of the key enabling functionalities for service robots. The best performing object detection approaches in computer vision exploit deep Convolutional Neural Networks (CNN) to simultaneously detect and categorize the objects of interest in cluttered scenes. Training of such models typically requires large amounts of annotated training data which is time consuming and costly to obtain. In this work we explore the ability of using synthetically generated composite images for training state-of-the-art object detectors, especially for object instance detection. We superimpose 2D images of textured object models into images of real environments at variety of locations and scales. Our experiments evaluate different superimposition strategies ranging from purely image-based blending all the way to depth and semantics informed positioning of the object models into real scenes. We demonstrate the effectiveness of these object detector training strategies on two publicly available datasets, the GMU-Kitchens and the Washington RGB-D Scenes v2. As one observation, augmenting some hand-labeled training data with synthetic examples carefully composed onto scenes yields object detectors with comparable performance to using much more hand-labeled data. Broadly, this work charts new opportunities for training detectors for new objects by exploiting existing object model repositories in either a purely automatic fashion or with only a very small number of human-annotated examples.