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

Torres-González, Arturo; Dios, J.R. Martínez-de; Ollero, A.

doi:10.3390/s140507684

Cited by 11 publications

(7 citation statements)

References 26 publications

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“…Despite its advantages few methods employ them since they involve significantly higher consumption of resources including the computational power required to integrate the higher number of measurements. In a previous work [28] we addressed the increment in resource consumption proposing a centralized EKF SLAM scheme that dynamically disconnects the integration of inter-beacon measurements in SLAM when the desired uncertainties have been met. In this paper we propose an efficient robot-beacons distributed SEIF scheme where beacons actively participate in gathering and integrating measurements in SLAM, sharing resource consumption and ensuring constant time execution.…”

Section: Related Workmentioning

confidence: 99%

Range-only SLAM for robot-sensor network cooperation

2017

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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.

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Section: Related Workmentioning

confidence: 99%

Range-only SLAM for robot-sensor network cooperation

2017

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“…In work [ 22 ] we proposed a RO-SLAM scheme that improves efficiency by choosing between two measurement gathering modes. Mode selection is performed using a simple centralized tool based on heuristics of robot and map estimation uncertainty: It does not consider resource consumption requirements, hence it cannot adapt to given resource consumption bounds.…”

Section: Related Workmentioning

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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“…Upon the base of robot-sensor network cooperation techniques, where sensor nodes (beacons) are used as landmarks, in [ 22 ] a range-only (RO) scheme is presented, that actuates over the measurement gathering process using mechanisms that dynamically modify the rate and variety of measurements that are integrated in an extended Kalman filter SLAM with auxiliary particle filters.…”

Section: Applications Based On Sensorsmentioning

confidence: 99%