Multi-robot Range-Only SLAM by Active Sensor Nodes for Urban Search and Rescue

Sun, Donglei; Kleiner, Alexander; Wendt, Thomas M.

doi:10.1007/978-3-642-02921-9_28

Cited by 20 publications

(11 citation statements)

References 13 publications

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“…With a high-end long-range RFID system, Ni et al [10] adjusted the power levels of the antenna to change detection ranges for estimating distances between the reader and tags. In the RFID systems that the received signal strength indication (RSSI) is available, the signal strength can be used for estimating distances between the reader and tags [13]. Ioho et al [6] took both RSSI and the detection probability into account using a hybrid measurement model.…”

Section: Related Workmentioning

confidence: 99%

Simultaneous localization and mapping using a short-range passive RFID reader with sparse tags in large environments

Chen

Wang

2010

2010 IEEE Workshop on Advanced Robotics and Its Social Impacts

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The radio frequency id ent i ficat i on (RFID) tec h nology has been studied and used to solve the localization and mapping problems in which most of studies use long-range active or passive RFID systems with multiple readers and dense tags. In this paper, we propose to use a short-range passive RFID reader to accomplish extended Kalman filter (EKF) based simultaneous localization and mapping (SLAM) with sparse tags in large indoor environments. We demonstrate that it is feasible to accomplish SLAM with sufficient accuracy using only odometry and short-range RFID data. The simulation and experimental results demonstrate the feasibility of the proposed system. I. INTRODU CTIO NThe radio frequency identification (RFID) technology has been applied in many applications such as storage man agement, personal guarding and public transporting system. The RFID applications are flexible and feasible because of RFID's fast detection capability and contactless identification with perfect data association. As data association is one of the most challenging problems in robot perception, the RFID technology has been used to solve a number of daunting robot perception problems such as mapping, localization and activity recognition. With a predefined or calibrated map of RFID tags, localization can be performed [10] [8] [1]. Hahnel et al. [4] and Joho et al. [6] proposed to learn the map of the RFID tags with perfect localization, and then use the built map to perform localization in which mapping and localization are solved separately in the two stages. RFID is also used and integrated with other sensors. In [3], data from a camera and RFID mounted on a pan-tilt unit of a robot are fused to accomplish person tracking where a RFID tagged person can be followed by the robot. As RFID tags also have a limited data storage capability, RFID can be used to deliver information in tasks such as activity recognition [11], search and rescue [7], and human robot interaction [9].In this work, a short-range passive RFID system is used to accomplish extended Kalman filter (EKF) based simultane ous localization and mapping (SLAM). Fig. 1 (a) shows the NTU-PAL4 robot in which the RFID reader was mounted on the right side of the robot and the SICK laser scanners were used to collect ground truth data. Fig. 1 (b) shows the RFID system used in this work. The RFID system is the RWM600 This work was partially supported by grants fr om Taiwan NSC ((a) The NTU-PAL4 robot.(b) The short-range passive RFID reader and tag. Fig. 1. The RFID system is mounted on the right side of the NTU-PAL4 robot. The SICK laser scanners on the top of the robot were used to collect ground truth data.module for ISOIIEC 15693 spec working on 13.56MHz channel with 5Hz reading frequency and the sensible range is only 0.1 meter. As the RFID devices in this work only have a centimeter level of detection range, only odometry data are available most of the time in the situations that RFID tags are deployed sparsely in environments. A new featurellandmark is added to the SLAM state...

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Section: Related Workmentioning

confidence: 99%

Simultaneous localization and mapping using a short-range passive RFID reader with sparse tags in large environments

Chen

Wang

2010

2010 IEEE Workshop on Advanced Robotics and Its Social Impacts

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“…In [26], the sparse sensor data issue was considered, and a SLAM solution was developed based on RF WSNs. In [58], a multi-robot range-only SLAM solution was proposed to solve the issue of map building with a team of autonomous robots and a WSN for urban search and rescue. The memory of the sensor nodes are used for map data exchange among mobile robots.…”

Section: Localizationmentioning

confidence: 99%

On the Cooperation between Mobile Robots and Wireless Sensor Networks

Shih

Capitán

Marrón

et al. 2014

Studies in Computational Intelligence

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Abstract. Employing cooperative heterogeneous systems can enrich application scenarios and achieve higher application performance. The combination of mobile robots and Wireless Sensor Networks (WSNs) is a good example of such cooperation, and many recent research results have highlighted the benefits of the marriage of these two technologies. The main objectives of this chapter include: (1) providing a survey on a variety of applications with cooperating mobile robots and WSNs based on the roles they play for interaction, and (2) elaborating different cooperative interactions of robots and WSNs in our ongoing project, PLAtform for the deployment and operation of heterogeneous NETworked cooperating objects (PLANET), which is an integrated framework of heterogeneous cooperative objects for network deployment and operations.

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“…Besides, knowing the location of beacons enables robot-sensor network cooperative missions of interest in these scenarios such as sensor node transportation and deployment [ 3 , 4 ], collection of data from sensors [ 5 , 6 ] or node remote powering [ 7 ]. The potential capabilities of robot-sensor network collaboration have recently originated significant interest in RO-SLAM methods that use sensor nodes as beacons [ 8 , 9 , 10 , 11 ]. However, most existing techniques consider beacons as passive landmarks disregarding the computational, communication and sensing capabilities with which they are actually endowed.…”

Section: Introductionmentioning

confidence: 99%

Robot-Beacon Distributed Range-Only SLAM for Resource-Constrained Operation

Torres-González

Dios

Ollero

2017

Sensors

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This work deals with robot-sensor network cooperation where sensor nodes (beacons) are used as landmarks for Range-Only (RO) Simultaneous Localization and Mapping (SLAM). Most existing RO-SLAM techniques consider beacons as passive devices disregarding the sensing, computational and communication capabilities with which they are actually endowed. SLAM is a resource-demanding task. Besides the technological constraints of the robot and beacons, many applications impose further resource consumption limitations. This paper presents a scalable distributed RO-SLAM scheme for resource-constrained operation. It is capable of exploiting robot-beacon cooperation in order to improve SLAM accuracy while meeting a given resource consumption bound expressed as the maximum number of measurements that are integrated in SLAM per iteration. The proposed scheme combines a Sparse Extended Information Filter (SEIF) SLAM method, in which each beacon gathers and integrates robot-beacon and inter-beacon measurements, and a distributed information-driven measurement allocation tool that dynamically selects the measurements that are integrated in SLAM, balancing uncertainty improvement and resource consumption. The scheme adopts a robot-beacon distributed approach in which each beacon participates in the selection, gathering and integration in SLAM of robot-beacon and inter-beacon measurements, resulting in significant estimation accuracies, resource-consumption efficiency and scalability. It has been integrated in an octorotor Unmanned Aerial System (UAS) and evaluated in 3D SLAM outdoor experiments. The experimental results obtained show its performance and robustness and evidence its advantages over existing methods.

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Multi-robot Range-Only SLAM by Active Sensor Nodes for Urban Search and Rescue

Cited by 20 publications

References 13 publications

Simultaneous localization and mapping using a short-range passive RFID reader with sparse tags in large environments

Simultaneous localization and mapping using a short-range passive RFID reader with sparse tags in large environments

On the Cooperation between Mobile Robots and Wireless Sensor Networks

Robot-Beacon Distributed Range-Only SLAM for Resource-Constrained Operation

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