Omnidirectional Sensor Research Articles

A closed form unwrapping method for a spherical omnidirectional view sensor

Abstract This article proposes a novel method of image unwrapping for spherical omnidirectional images acquired through a non-single viewpoint (NSVP) omnidirectional sensor. It has three key steps i.e. (1) calibrate the camera to obtain parameters describing the spherical omnidirectional sensor, (2) map world points onto mirror points and, subsequently, onto image points, and (3) set up the projection plane for the final image unwrapping. Based on the projection plane selected, the algorithm is able to produce three common forms of unwrapping, namely cylindrical panoramic, cuboid panoramic, and ground plane view using closed form mapping equations. The motivation for developing this technique is to address the complexity in using a NSVP omnidirectional sensor and ultimately encouraging its application in robotics field. One of the main advantages of a NSVP omnidirectional sensor is that the mirror can often be obtained at a lower price as compared to the single viewpoint counterpart.

LEND neutron data processing for the mapping of the Moon

The Lunar Exploration Neutron Detector (LEND) aboard the Lunar Reconnaissance Orbiter (LRO) has been mapping the neutron flux from the Moon since July 2009. LEND has four different types of neutron detectors which allow a comprehensive study of lunar neutron emission: a pair of omnidirectional sensors for thermal and epithermal neutrons, a pair of Doppler filter sensors for thermal neutrons, four collimated sensors of epithermal neutrons, and a sensor of high‐energy neutrons. This paper describes the data reduction procedures to convert the raw data into the higher‐level PDS products containing neutron‐counting rate maps of the Moon.

Minimal exposure path algorithms for directional sensor networks

As a fundamental problem of wireless sensor networks, the minimal exposure path problem corresponding to the sensor network's worst-case coverage plays an important role in the applications for detecting intrusions. However, most existing works about minimal exposure path are based on omnidirectional sensors. In contrast, this paper studies the minimal exposure path problem for two different types of directional sensing models: the binary sector model and directional sensitivity model. For the binary sector model, we construct a special Voronoi diagram, called sector centroids-based Voronoi diagram, to transform the minimal exposure path problem from a continuous geometric problem into a discrete geometric problem. By using the sector centroids-based Voronoi diagram, we develop an approximate algorithm to find the minimal exposure path in the sensors deployment field. For the directional sensitive model, we formulate the minimal exposure path problem by using two sensing intensity functions: all-sensor intensity function and maximum-sensor intensity function and then generate two weighted grids to convert the minimal exposure path problem into two discrete geometric problems. On the basis of the aforementioned weighted grids, we also develop two approximation algorithms to find the minimal exposure path for the directional sensitive model. We conduct extensive experiment simulations to validate and evaluate our proposed models and algorithms. Copyright © 2012 John Wiley & Sons, Ltd.

Autonomous Variable-Resolution Map Building for Mobile Robots in Unknown Environments

SUMMARY This paper presents a method that allows simultaneous map building and path planning for mobile robots in unknown environments. A graphical representation of a workspace in variable resolutions is constructed using measurement data obtained by omnidirectional distance sensors. At the same time, a search for a feasible path to the target destination is executed using the constructed graph map. The proposed method is evaluated by performing simulations and experiments using an omnidirectional mobile robot equipped with laser range finders. © 2013 Wiley Periodicals, Inc. Electr Eng Jpn, 186(4): 59–69, 2014; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/eej.22493

1A1-C01 全方位センシング可能なネット状近接覚センサの開発 : 素子角度差を用いた反射率・形状・姿勢によらない距離検出手法(触覚と力覚(1))

1A1-C01 全方位センシング可能なネット状近接覚センサの開発 : 素子角度差を用いた反射率・形状・姿勢によらない距離検出手法(触覚と力覚(1))

An FPGA-Based Omnidirectional Vision Sensor for Motion Detection on Mobile Robots

This work presents the development of an integrated hardware/software sensor system for moving object detection and distance calculation, based on background subtraction algorithm. The sensor comprises a catadioptric system composed by a camera and a convex mirror that reflects the environment to the camera from all directions, obtaining a panoramic view. The sensor is used as an omnidirectional vision system, allowing for localization and navigation tasks of mobile robots. Several image processing operations such as filtering, segmentation and morphology have been included in the processing architecture. For achieving distance measurement, an algorithm to determine the center of mass of a detected object was implemented. The overall architecture has been mapped onto a commercial low-cost FPGA device, using a hardware/software co-design approach, which comprises a Nios II embedded microprocessor and specific image processing blocks, which have been implemented in hardware. The background subtraction algorithm was also used to calibrate the system, allowing for accurate results. Synthesis results show that the system can achieve a throughput of 26.6 processed frames per second and the performance analysis pointed out that the overall architecture achieves a speedup factor of 13.78 in comparison with a PC-based solution running on the real-time operating system xPC Target.

Coverage and activity management of wireless video sensor networks for surveillance applications

Wireless Video Sensor Networks consist in nodes with miniaturised cameras. Unlike omni-directional sensors, the sensing region of a video node is limited to the field of view of its camera. This paper addresses the issues of building an intrusion detection system based on these video sensors and presents two contributions. The first contribution presents the performance of various cover set construction strategies for determining for each sensor its level of redundancy. Simulation results showed the performance of the strategies in terms of percentage of coverage, network lifetime, intrusion stealth time and number of intrusion detection. The second contribution addresses efficient activity management of nodes for mission-critical surveillance applications. The proposed approach dynamically determines a sensor's frame capture rate. We showed that varying the criticality level dynamically during the network operation increases the network lifetime while maintaining a high level of service. With reinforcement behaviour the network lifetime is further increased.

HiPER 3-D: An Omnidirectional Sensor for High Precision Environmental 3-D Reconstruction

The environmental 3-D reconstruction is a strategic task in many contexts, above all, in infrastructure inspection and vehicle/robot automatic motion. In this paper, we present a patented sensor capable to recover 3-D data with a very high profile acquisition rate and perform an omnidirectional highly accurate environmental reconstruction: these skills are allowed by a profilometric laser approach coupled to a catadioptric system. After a presentation of the sensor's functionality, its sensitivity is theoretically analyzed both in terms of maximum and medium error. This, not only for proving the sensor's accuracy (which, anyway, has been also experimentally tested), but also for defining a design strategy which optimally sets up its geometrical parameters. Performance of a prototypal implementation of the sensor, as well as a calibration technique, are presented, and several indoor and outdoor 3-D reconstructions are shown.

Stereo bearings-only tracking of a wheelchair from a robot

Abstract This project deals with technical assistance for people of reduced mobility. The goal of this work is to have a mobile platform with an embedded prehensive arm (MANUS®) track a wheelchair. The use of this mobile unit in relation to the patient’s wheelchair is carried out on a master-slave basis. This study, therefore, has a plural-disciplinary nature: Science for the Engineer, Human, Social and Clinical Sciences. To ensure the tracking of the wheelchair by the mobile platform, we applied a stereo bearings-only tracking (BOT) paradigm to a single moving target. The observer, the mobile platform (robot), is equipped with two omnidirectional vision sensors, that each provides output with which we compute two bearing angles. Having two bearing angles makes it possible to track a manoeuvring target. In this paper we will show how we take advantage of the multi-sensor data fusion to improve the BOT process and hence obtain a more reliable wheelchair tracking.

Fusion of complementary catadioptric panoramic images based on nonsubsampled contourlet transform

The problem of low and nonuniform resolution has long been a topic of research in omnidirectional imaging. A novel fusion method based on nonsubsampled contourlet transform is proposed in this paper to fuse the pair of complementary panoramic images unwrapped from the inner and outer of the omni-image. This work can be considered as a continuation research of the complementary-structure catadioptric imaging technique based on multiple mirrors. Specifically, the high-frequency details are extracted by nonsubsampled directional filters only in the horizontal and vertical directions, since the complementarity mostly exists in two orthogonal directions. The horizontal and vertical high-frequency coefficients of the fused image are selected from the panoramic image which has the advantage in the corresponding direction. The fusion rule for the low-frequency coefficients is an improved "selecting" scheme with considering the high-frequency directional vector. Simulation experiments based on decimation and interpolation are implemented using the proposed fusion method, which outperforms other existing fusion methods in terms of both visual quality and objective evaluation. The proposed fusion method has demonstrated on experiments that both indoor and outdoor of real-scene imaging can gain better performance by using our prototype omnidirectional sensor.

Panoramic Vision Mine Safety Monitoring System Based on WLAN

For the requirements of complex environment and safe-management in the coal mine, this paper presents a new Panoramic Vision Mine Safety Monitoring System Based on WLAN. the monitoring system use a binocular omni-directional vision sensor(ODVS)[1] coordinated with the WLAN or LAN network to transmit images, information, commands or data. The system can also get the panoramic vision in the coal mine from any miner at any time. The miners’ identities will be binding with the ODVS and WLAN protocol. Then the monitoring system will use the binding information to position and track miners. This is a new reliable monitoring system which can monitor the coal mine environment security, the coal mine producing security, the coal mine transport security, the miner security at the same time, and these provide the system with expecting application value and market outlook.

A Geometric Algebra Model for the Image Formation Process of Paracatadioptric Cameras

Omnidirectional vision sensors capture a wide field of view than can benefit many robotic applications. One type of omnidirectional vision sensor is the paracatadioptric. A paracatadioptric sensor combines a parabolic mirror and a camera inducing an orthographic projection. This combination provides a wide field of view while maintaining the single center of projection which is a desirable property of these sensors. Furthermore, lines are projected as circles on the paracatadioptric image plane. In contrast with traditional perspective cameras the image formation process of paracatadioptric sensors is no longer linear. However in this paper we present a model which is able to linearize it. This linearization is based on the fact that the paracatadioptric projection can be represented by a sphere inversion, that belongs to the conformal group $\mathcal{R}^{n}$ which is isomorphic to the Lorentz group in $\mathcal{R}^{n+1}$ . Thus a nonlinear conformal transformation can be represented with an equivalent linear Lorenz transformation, which can be represented as a versor in the CGA. Therefore the present model can be applied algebraically not only to points, but also to point-pairs, lines, circles in the same way to all them and in a linear form. The benefits of the proposed method will be reflected on the development of complex applications that use paracatadioptric sensors.

Some Complexity Results for Metric View Planning Problem With Traveling Cost and Visibility Range

In this paper, we consider the problem where a point robot in a 2D or 3D environment equipped with an omnidirectional range sensor of finite range D is asked to cover a set of surface patches, while minimizing the sum of view cost, proportional to the number of viewpoints planned, and the travel cost, proportional to the length of path traveled. We call it the Metric View Planning Problem with Traveling Cost and Visibility Range or Metric TVPP in short. We present a complexity result for the problem, i.e., we show that the Metric TVPP cannot be approximated within O(log m) ratio by any polynomial algorithm, where m is the number of surface patches to cover. We then analyze a variant of an existing decoupled two-level algorithm of first solving the view planning problem to get an approximate solution, and then solving, again using an approximation algorithm, the Metric traveling salesman problem to connect the planned viewpoints. We then present performance bounds for this two-level decoupled algorithm, i.e., we show that it has an approximation ratio of O(log m). Thus, it asymptotically achieves the best approximation ratio one can hope for.

Nonsingle viewpoint omni-stereo depth estimation via space layer labeling

An omni-directional stereo system has a wider field-of-view than conventional cameras, and is widely used in many applications such as robots navigation, depth estimation, and 3D reconstruction. Existing approaches usually use single viewpoint (SVP) systems as the imaging sensor. However, literature proves that an efficient SVP of an omni-directional system can only be achieved with precisely aligned mirrors of parabolic or hyperbolic profile. This enforces rigorous restriction on the configuration of camera and mirrors. In fact, some other profiles, though they do not have the SVP property, are desirable for certain reasons such as cheaper cost and more practical implementation. Therefore, in this paper, we propose both a typical nonsingle viewpoint (non-SVP) omni-directional stereo sensor and its corresponding depth estimation method based on graph-cuts optimization. The sensor comprises a perspective camera and two separate reflective mirrors that could be any radially-symmetric ones. To formulate the depth estimation more consistent with the proposed sensor, we divide the depth space of scenes with a sequence of virtual coaxial cylindrical layers, and model depth estimation as a labeling problem. In the labeling procedure, by considering the characteristics of an omni-directional image, we further devise novel tangential-neighborhood system, radial-neighborhood system, and depth-gradual-changing smoothness constraint which perform better than traditional ones. Depth estimation and 3D reconstruction for both synthesis and real scenes justify the effectiveness of the proposed method.

Complementary-structure catadioptric omnidirectional sensor design for resolution enhancement

Considering the problem of low and nonuniform resolution in omnidirectional imaging, a complementary catadioptric imaging method is proposed by using multiple mirrors. Due to the reflection of mirrors, each spatial object has two imaging positions, in the inner and outer of sampled omnidirectional images, respectively, which are generated from two different optical paths. For instance, a prototype of omnidirectional sensor based on complementary double imaging is designed, which contains two convex mirrors (cone and hyperboloid) in conjunction with a plane mirror. The design constraints and optical geometric model of imaging are analyzed in detail. By mathematical analysis, two improvements of resolution have been achieved: (1) more uniform distribution of resolution and (2) complementary resolution distribution in the radial and tangential directions between the inner and outer. To prove and demonstrate the remarkable complementarity, a simple fusion experiment based on wavelet decomposition and reconstruction is performed on a pair of cylindrical panoramic images unwrapped from an omnidirectional image.

On coverage issues in directional sensor networks: A survey

The coverage optimization problem has been examined thoroughly for omni-directional sensor networks in the past decades. However, the coverage problem in directional sensor networks (DSN) has newly taken attraction, especially with the increasing number of wireless multimedia sensor network (WMSN) applications. Directional sensor nodes equipped with ultrasound, infrared, and video sensors differ from traditional omni-directional sensor nodes with their unique characteristics, such as angle of view, working direction, and line of sight (LoS) properties. Therefore, DSN applications require specific solutions and techniques for coverage enhancement. In this survey article, we mainly aim at categorizing available coverage optimization solutions and survey their problem definitions, assumptions, contributions, complexities and performance results. We categorize available studies about coverage enhancement into four categories. Target-based coverage enhancement, area-based coverage enhancement, coverage enhancement with guaranteed connectivity, and network lifetime prolonging. We define sensing models, design issues and challenges for directional sensor networks and describe their (dis)similarities to omni-directional sensor networks. We also give some information on the physical capabilities of directional sensors available on the market. Moreover, we specify the (dis)advantages of motility and mobility in terms of the coverage and network lifetime of DSNs.

Target Person Identification and Following Based on Omnidirectional Camera and LRF Sensor Fusion from a Moving Robot

In this paper, we introduce an approach to identify and follow a target person for a service robot application. The robot is equipped with LRF and omnidirectional camera. Our approach is based on multisensor fusion in which a person is identified using the panoramic image and tracked using the Laser Range Finder (LRF). A target person selection is implemented to improve the identification when multiple candidates are detected. Our approach is successfully implemented on a mobile robot. A simplified target person following behavior is implemented to focus on the proposed method’s efficiency. Several experiments are conducted and showed the effectiveness of our approach to identify and follow human in indoor environments.

Haar invariant signatures and spatial recognition using omnidirectional visual information only

This paper describes a method for spatial representation, place recognition and qualitative self-localization in dynamic indoor environments, based on omnidirectional images. This is a difficult problem because of the perceptual ambiguity of the acquired images, and their weak robustness to noise, geometrical and photometric variations of real world scenes. The spatial representation is built up invariant signatures using Invariance Theory where we suggest to adapt Haar invariant integrals to the particular geometry and image transformations of catadioptric omnidirectional sensors. It follows that combining simple image features in a process of integration over visual transformations and robot motion, can build discriminant percepts about robot spatial locations. We further analyze the invariance properties of the signatures and the apparent relation between their similarity measures and metric distances. The invariance properties of the signatures can be adapted to infer a hierarchical process, from global room recognition to local and coarse robot localization. The approach is validated in real world experiments and compared to some local and global state-of-the-art methods. The results demonstrate a very interesting performance of the proposed approach and show distinctive behaviors of global and local methods. The invariant signature method, while being very time and memory efficient, provides good separability results similarly to approaches based on local features.

Diverse traversability mapping using hybrid vision system for lunar rover

Autonomous functions including mapping and path planning are very important to ensure efficient exploration and safe navigation of unmanned rover. Conventional navigation schemes based on equipments such as GPS and magnetic sensors have been proven to be ineffective on lunar surface, while sophisticated dead reckoning approach lacks accuracy in loose soil due to slippage. In this paper, we propose a hybrid vision system (HVS) which consists of one stereo and one omnidirectional vision sensor, attempting to integrate the advantages of diverse vision systems. The hierarchical mapping reconstructs environment through HVS in different resolutions and structures, named qualitative, rough and detailed map. These maps are employed for path planning which navigates the rover towards specified destinations autonomously, avoiding obstacles in its way. The HVS aims at providing more efficient and safe navigation. Simulations and experiments demonstrate the functionality and advantage of our approach.

Sensor Deployment for Network-Like Environments

This technical note considers the problem of optimally deploying omnidirectional sensors, with potentially limited sensing radius, in a network-like environment. This model provides a compact and effective description of complex environments as well as a proper representation of road or river networks. We present a two-step procedure based on a discrete-time gradient ascent algorithm to find a local optimum for this problem. The first step performs a coarse optimization where sensors are allowed to be clustered, to move in the plane, to vary their sensing radius and to make use of a reduced model of the environment called collapsed network. The sensors' positions found in the first step are then projected on the network and used in the second finer optimization, where sensors are constrained to move only on the network. The second step can be performed online, in a distributed fashion, by sensors moving in the real environment, and can make use of the full network as well as of the collapsed one. The adoption of a less constrained initial optimization has the merit of reducing the negative impact of the presence of a large number of local optima. The effectiveness of the presented procedure is illustrated by a simulated deployment problem in an airport environment.