In this paper, the Relative Pose based Redundancy Removal (RPRR) scheme is presented, which has been designed for mobile RGB-D sensor networks operating under bandwidth-constrained operational scenarios. The scheme considers a multiview scenario in which pairs of sensors observe the same scene from different viewpoints, and detect the redundant visual and depth information to prevent their transmission leading to a significant improvement in wireless channel usage efficiency and power savings. We envisage applications in which the environment is static, and rapid 3D mapping of an enclosed area of interest is required, such as disaster recovery and support operations after earthquakes or industrial accidents. Experimental results show that wireless channel utilization is improved by 250 and battery consumption is halved when the RPRR scheme is used instead of sending the sensor images independently.
The algorithms mentioned above focus only on color (RGB) data. Only a few studies have been reported @cite_37 @cite_26 @cite_32 that use RGB-D sensors in VSNs, as their use in networked robotics scenarios has not yet become widespread. Consequently, our extensive review of the research literature has not identified any earlier studies that attempt to develop an efficient coding system that aims to maximize the bandwidth usage and minimize the energy consumption for RGB-D equipped VSNs.
@cite_37 @cite_26 @cite_32
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Color-depth cameras (RGB-D cameras) have become the primary sensors in most robotics systems, from service robotics to industrial robotics applications. Typical consumer-grade RGB-D cameras are provided with a coarse intrinsic and extrinsic calibration that generally does not meet the accuracy requirements needed by many robotics applications [e.g., highly accurate three-dimensional (3-D) environment reconstruction and mapping, high precision object recognition, localization, etc.]. In this paper, we propose a human-friendly, reliable, and accurate calibration framework that enables to easily estimate both the intrinsic and extrinsic parameters of a general color-depth sensor couple. Our approach is based on a novel two components error model. This model unifies the error sources of RGB-D pairs based on different technologies, such as structured-light 3-D cameras and time-of-flight cameras. Our method provides some important advantages compared to other state-of-the-art systems: It is general (i.e., well suited for different types of sensors), based on an easy and stable calibration protocol, provides a greater calibration accuracy, and has been implemented within the robot operating system robotics framework. We report detailed experimental validations and performance comparisons to support our statements. Many applications in visual sensor networks (VSNs) demand the low-cost wireless transmission of video data. In this context, distributed video coding (DVC) has proven its potential to achieve state-of-the-art compression performance while maintaining low computational complexity of the encoder. Despite their proven capabilities, current DVC solutions overlook hardware constraints, and this renders them unsuitable for practical implementations. This paper introduces a DVC architecture that offers highly efficient wireless communication in real-world VSNs. The design takes into account the severe computational and memory constraints imposed by practical implementations on low-resolution visual sensors. We study performance-complexity trade-offs for feedback-channel removal, propose learning-based techniques for rate allocation, and investigate various simplifications of side information generation yielding real-time decoding. The proposed system is evaluated against H.264 AVC intra, Motion-JPEG, and our previously designed DVC prototype for low-resolution visual sensors. Extensive experimental results on various data show significant improvements in multiple configurations. The proposed encoder achieves real-time performance on a 1k-pixel visual sensor mote. Real-time decoding is performed on a Raspberry Pi single-board computer or a low-end notebook PC. To the best of our knowledge, the proposed codec is the first practical DVC deployment on low-resolution VSNs. Although RGB-D sensors have enabled major break-throughs for several vision tasks, such as 3D reconstruction, we have not attained the same level of success in high-level scene understanding. Perhaps one of the main reasons is the lack of a large-scale benchmark with 3D annotations and 3D evaluation metrics. In this paper, we introduce an RGB-D benchmark suite for the goal of advancing the state-of-the-arts in all major scene understanding tasks. Our dataset is captured by four different sensors and contains 10,335 RGB-D images, at a similar scale as PASCAL VOC. The whole dataset is densely annotated and includes 146,617 2D polygons and 64,595 3D bounding boxes with accurate object orientations, as well as a 3D room layout and scene category for each image. This dataset enables us to train data-hungry algorithms for scene-understanding tasks, evaluate them using meaningful 3D metrics, avoid overfitting to a small testing set, and study cross-sensor bias. Recent advances in 3D sensing technologies make it possible to easily record color and depth images which together can improve object recognition. Most current methods rely on very well-designed features for this new 3D modality. We introduce a model based on a combination of convolutional and recursive neural networks (CNN and RNN) for learning features and classifying RGB-D images. The CNN layer learns low-level translationally invariant features which are then given as inputs to multiple, fixed-tree RNNs in order to compose higher order features. RNNs can be seen as combining convolution and pooling into one efficient, hierarchical operation. Our main result is that even RNNs with random weights compose powerful features. Our model obtains state of the art performance on a standard RGB-D object dataset while being more accurate and faster during training and testing than comparable architectures such as two-layer CNNs.