RSSI-based relative localisation for mobile robots

Abstract

a b s t r a c tIn this work, we develop an anchor-less relative localisation algorithm aimed to be used in multi-robot teams. The localisation is performed based on the Received Signal Strength Indicator (RSSI) readings collected from the messages exchanged between nodes. We use the RSSI as a rough estimate of the inverse of distance between any pair of communicating nodes, and we claim that such estimates provide a coarse information of the nodes relative localisation that is still suitable to support several coordination tasks. In addition, we introduce a relative velocity estimation framework based on the RSSI measurements. This framework uses consecutive distance measurements and position estimates to provide the relative velocity vectors for all the nodes in the network.To accomplish this, we propose using a Kalman filter and the Floyd-Warshall algorithm to generate smooth RSSI pairwise signal distance for all nodes. Then we use Multidimensional Scaling to obtain relative positions from the pairwise distances. Finally, due to anchor unavailability, relative positions are adjusted to reflect the continuous mobility by using geometric transformations, thus obtaining smoother trajectories for mobile nodes. This allows us to estimate velocity and to establish a correspondence between orientation in the physical world and in the relative coordinates system.Additionally, we study the impact of several parameters in calculating the network topology, namely different approaches to provide a symmetric distances matrix, the period of the matrix dissemination, the use of synchronisation of the transmissions, and the filtering of the RSSI data. Experimental results, with a set of MicaZ motes, show that the period of matrix dissemination is the most relevant of the parameters, specifically with larger periods providing the best results, however, shorter periods are shown to be possible as long as the transmissions are synchronised.

A Node Localization Verification Model for WSN

“…The pairwise distance of neighbor nodes is d ij which can be acquired by Eq. (1) in an ideal environment without noise-like errors is [7]:…”

“…According to whether the localization process needs to measure the distance between nodes, the localization algorithms fallen into range-based localization algorithm [5] and range-free localization algorithm [6]. In general, range-based localization algorithms fulfill the localization by ranging the distances between nodes through their received signal strength indicator (RSSI), time of arrival (TOA) or angle of arrival (AOA) [7] and so on. Among them, RSSI-based ranging methods are of the most practical and applicable.…”

Towards Robot Localization Using Bluetooth Low Energy Beacons RSSI Measures

“…Measuring some signal features should be possible to estimate distance between a receptor and a BLE beacon. The distance estimation could be done using the following characteristics: RSSI [7,8,9], Time of Arrival (ToA) [10,11], the Time Difference of Arrival (TDoA) [12,13], the Angle of Arrival (AoA) [14,15], and the Time of Flight (ToF) [16,17]. The only procedures available for BLE beacons are those based on RSSI.…”

Collaborative Localization and Location Verification in WSNs

“…Furthermore, according to whether the localization process needs to measure the distance between nodes, the localization algorithms can be range-based localization algorithm [6] and range-free localization algorithm [7]. Generally, range-based localization algorithms complete the establishment of the location coordinates system by measuring the distances between nodes through their received signal strength indicator (RSSI) [8], time of arrival (TOA) [9] or angle of arrival (AOA) [10] etc. , Among them, RSSI-based localization algorithms are the most practical and applicable.…”