Arturo Torres-González

Cooperation Between UAS and Wireless Sensor Networks for Efficient Data Collection in Large Environments

This paper describes a method for collection of data from Wireless Sensor Network (WSN) deployed in large environments using Unmanned Aerial Systems (UAS). Unlike existing approaches, in which the WSN and the UAS act as independent units, the main novelty of the proposed method is that UAS and WSN cooperate to increase the performance of the mission. The proposed method presents two main cooperative behaviors: (1) the results of the WSN operation are used to update the UAS flight plan and; (2) the UAS trajectory is considered in the operation of the WSN in order to improve the data collection performance. The proposed method outperforms non-cooperative UAS-based collection approaches and traditional ground multi-hop collection schemes. The method has been experimented in the airfield of Bellavista in Seville (Spain) in March 2011.

Efficient robot-sensor network distributed SEIF range-only SLAM

This paper is motivated by schemes of 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, computing and communication capabilities they are actually endowed with. This paper proposes a Range-Only scheme based on Sparse Extended Information Filters (SEIF) that efficiently exploits their capabilities. The robot computes the SLAM prediction stage and distributes the update stage among beacons within its sensing area. The proposed scheme naturally integrates robot-beacon and inter-beacon measurements, significantly improving map and also robot estimations. Our scheme inherits from SEIF its efficiency and scalability and further reduces robot computational burden by exploiting the beacons computing capability. As a result, it has lower error and lower computer requirements than traditional methods. This paper presents the scheme, evaluates and compares its performance in simulations and real experiments.

An Adaptive Scheme for Robot Localization and Mapping with Dynamically Configurable Inter-Beacon Range Measurements

This work is motivated by robot-sensor network cooperation techniques where sensor nodes (beacons) are used as landmarks for range-only (RO) simultaneous localization and mapping (SLAM). This paper presents a RO-SLAM scheme that actuates over the measurement gathering process using mechanisms that dynamically modify the rate and variety of measurements that are integrated in the SLAM filter. It includes a measurement gathering module that can be configured to collect direct robot-beacon and inter-beacon measurements with different inter-beacon depth levels and at different rates. It also includes a supervision module that monitors the SLAM performance and dynamically selects the measurement gathering configuration balancing SLAM accuracy and resource consumption. The proposed scheme has been applied to an extended Kalman filter SLAM with auxiliary particle filters for beacon initialization (PF-EKF SLAM) and validated with experiments performed in the CONET Integrated Testbed. It achieved lower map and robot errors (34% and 14%, respectively) than traditional methods with a lower computational burden (16%) and similar beacon energy consumption.

Integrating Internode Measurements in Sum of Gaussians Range Only SLAM

This paper proposes a Range Only-SLAM method based on Sums of Gaussians (SoG) that integrates direct robot-beacon measurements together with measurements between static beacons. It exploits the fact that most commercial off-the-shelf beacons can communicate and organize into sensor networks and can compute range measurements to other beacons. The proposed method adopts a scheme based on Sums of Gaussians, which allows integrating direct robot-beacon measurements in an undelayed way, which is a significant advantage particularly in environments where the robot has bad odometry. The method has been implemented and validated in real experiments performed in the CONET Robot-WSN Integrated Testbed. The proposed method achieves a reduction of 70% in map error and significant improvement in robot pose accuracy when compared to traditional schemes.

Robot-WSN Cooperation for Scalable Simultaneous Localization and Mapping

This chapter proposes a scalable SLAM method that uses range measurements sensed by Wireless Sensor Networks (WSN) nodes. It integrates direct robot-node range measurements as well as measurements static nodes take from other nodes –internode measurements– exploiting WSN nodes capability of organizing into networks. To cope with the high number of measurements, the method adopts an PF-EIF SLAM filter, significantly more scalable and efficient than traditional schemes based on EKF. The integration and use of internode measurements can significantly improve map and robot estimations accuracy. It can also anticipate the deployment and convergence of the Particle Filters (PFs), resulting in lower computational burden. The proposed method has been compared with traditional schemes based on EKF both in simulation and in experiments carried out in the CONET Integrated Robot-WSN Testbed.

Range-only SLAM for robot-sensor network cooperation

This work is motivated by schemes of robot-sensor network cooperation where sensor nodes—beacons—are used as landmarks for range-only (RO) simultaneous localization and mapping (SLAM). In most existing RO-SLAM techniques beacons are considered as passive devices ignoring the capabilities they are actually endowed with. This paper proposes a RO-SLAM scheme that distributes the measurements gathering and integration between the beacons surrounding the robot. It naturally integrates inter-beacon measurements, significantly improving map and robot estimations and speeding up beacon initialization. The proposed scheme is based on sparse extended information filter (SEIF) and it is proven that it preserves the constant time and sparsity properties of SEIF and thus, inherits its efficiency and scalability. As a result, our scheme has lower robot and map estimation errors, faster beacon initialization and lower computer requirements than existing methods. This paper experimentally validates and evaluates the proposed method for 3D SLAM using an octorotor.

Combining Unmanned Aerial Systems and Sensor Networks for Earth Observation

The combination of remote sensing and sensor network technologies can provide unprecedented earth observation capabilities, and has attracted high R&D interest in recent years. However, the procedures and tools used for deployment, geo-referenciation and collection of logged measurements in the case of traditional environmental monitoring stations are not suitable when dealing with hundreds or thousands of sensor nodes deployed in an environment of tenths of hectares. This paper presents a scheme based on Unmanned Aerial Systems that intends to give a step forward in the use of sensor networks for environment observation. The presented scheme includes methods, tools and technologies to solve sensor node deployment, localization and collection of measurements. The presented scheme is scalable—it is suitable for medium–large environments with a high number of sensor nodes—and highly autonomous—it is operated with very low human intervention. This paper presents the scheme including its main components, techniques and technologies, and describes its implementation and evaluation in field experiments.

An Efficient Fast-Mapping SLAM Method for UAS Applications Using Only Range Measurements

This paper deals with 3D Simultaneous Localization and Mapping (SLAM), where the UAS uses only range measurements to build a local map of an unknown environment and to self-localize in that map. In the recent years Range Only (RO) SLAM has attracted significant interest, it is suitable for non line-of-sight conditions and bad lighting, being superior to visual SLAM in some problems. However, some issues constrain its applicability in practical cases, such as delays in map building and low map and UAS estimation accuracies. This paper proposes a 3D RO-SLAM scheme for UAS that specifically focuses on improving map building delays and accuracy levels without compromising efficiency in the consumption of resources. The scheme integrates sonar measurements together with range measurements between the robot and beacons deployed in the scenario. The proposed scheme presents two main advantages: (1) it integrates direct range measurements between the robot and the beacons and also range measurements between beaco...

Accurate fast-mapping Range-Only SLAM for UAS applications

This paper deals with 3D Range-Only (RO) Simultaneous Localization and Mapping (SLAM), where the UAS integrates range measurements to a number of beacons in the environment. In the recent years RO-SLAM has attracted significant interest, it is suitable for non line-of-sight conditions and/or bad lighting, being superior to visual SLAM in some problems. However, some issues constrain its applicability in practical cases, such as delays in map building and low map and UAS estimation accuracies. This paper proposes a 3D RO-SLAM scheme for UAS that specifically focuses on improving map building delays and accuracy levels without penalizing efficiency in the consumption of resources. The proposed scheme presents two main advantages: 1) it integrates inter-beacon measurements, which significantly reduces map building times and improves map and UAS localization accuracies; and 2) it is endowed with a supervisory module that self-adapts the measurements that are integrated in SLAM reducing resource consumption. Experimental validation in field experiments with an octorotor UAS showed that the proposed scheme improved map building times in 80%, map accuracy in 31% and UAS localization accuracy in 18%.

Exploiting Multi-hop Inter-beacon Measurements in RO-SLAM

This chapter presents a Range Only (RO) Simultaneous Localization and Mapping (SLAM) scheme that integrates multi-hop inter-beacon range measurements. While few SLAM schemes use inter-beacon measurements, to the best of our knowledge, none of them integrates multi-hop inter-beacon measurements. In our scheme the robot gathers inter-beacon measurements with configurable hop number so that it can integrate measurements between beacons far beyond the robot’s sensing range. This chapter analyzes the impact of integrating in SLAM inter-beacon measurements with different hop numbers and evaluates its performance and robustness to measurement and odometry noise levels. It also validates its results in real experiments. It shows that the advantages of using inter-beacon measurements increase with measurements with higher hop numbers.