Abstract
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.
Highlights
This paper deals with range only simultaneous localization and mapping (SLAM), in which a robot integrates range measurements of a set of static landmarks in order to build a local map from an unknown environment and to simultaneously self-locate in that map
It shows: the values of the smoothed robot trace used as robot error estimation; the measurement gathering state provided by the SLAM supervisor; and the robot and map location errors at each time, represented in blue and red, respectively
This paper presents a RO-SLAM scheme that actuates over the measurement gathering process
Summary
This paper deals with range only SLAM, in which a robot integrates range measurements of a set of static landmarks in order to build a local map from an unknown environment and to simultaneously self-locate in that map. The proposed scheme exploits the capability of the beacons of taking range measurements to other beacons and transmitting them using ad hoc protocols It integrates in SLAM direct measurements, as traditional methods, and inter-beacon measurements with different depth levels. It has been validated in experiments performed in the CONET (Cooperating Objects Network of Excellence) Integrated Testbed (http://conet.us.es) [10] In these experiments, it achieved significantly lower map and robot errors than the traditional RO-SLAM schemes with lower computational burden and similar beacon energy consumption.
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