We present a novel framework for collaboration amongst a team of robots performing Pose Graph Optimization (PGO) that addresses two important challenges for multi-robot SLAM: that of enabling information exchange "on-demand" via active rendezvous, and that of rejecting outlier measurements with high probability. Our key insight is to exploit relative position data present in the communication channel between agents, as an independent measurement in PGO. We show that our algorithmic and experimental framework for integrating Channel State Information (CSI) over the communication channels, with multi-agent PGO, addresses the two open challenges of enabling information exchange and rejecting outliers. Our presented framework is distributed and applicable in low-lighting or featureless environments where traditional sensors often fail. We present extensive experimental results on actual robots showing both the use of active rendezvous resulting in error reduction by 6X as compared to randomly occurring rendezvous and the use of CSI observations providing a reduction in ground truth pose estimation errors of 32 . These results demonstrate the promise of using a combination of multi-robot coordination and CSI to address challenges in multi-agent localization and mapping -- providing an important step towards integrating communication as a novel sensor for SLAM tasks.
Additionally, there is a large body of work focused on light-weight position estimation from Radio Frequency (RF) signals. For instance, RF localization has been successfully applied to positioning when access to GPS signals are unavailable, such as indoor environments @cite_10 @cite_8 , but requires the presence of known anchors. Decentralized localization without the use of beacons or anchor points can be achieved through range @cite_20 and vectorial distance estimation @cite_29 via time-of-arrival and time-difference-of-arrival techniques for multilateration. Angle-of-arrival measurements can also be used for estimating orientation @cite_26 . Synthetic aperture radar (SAR) techniques have been used for indoor positioning in the presence of multipath and alleviate the need for bulky multi-antenna arrays @cite_0 @cite_24 @cite_16 @cite_25 , which shows great promise for robotic applications.
@cite_26 @cite_8 @cite_29 @cite_0 @cite_24 @cite_16 @cite_10 @cite_25 @cite_20
2013503669 2005059864 2011502582 2553223022
Recent years have seen the advent of new RF-localization systems that demonstrate tens of centimeters of accuracy. However, such systems require either deployment of new infrastructure, or extensive fingerprinting of the environment through training or crowdsourcing, impeding their wide-scale adoption. We present Ubicarse, an accurate indoor localization system for commodity mobile devices, with no specialized infrastructure or fingerprinting. Ubicarse enables handheld devices to emulate large antenna arrays using a new formulation of Synthetic Aperture Radar (SAR). Past work on SAR requires measuring mechanically controlled device movement with millimeter precision, far beyond what commercial accelerometers can provide. In contrast, Ubicarse's core contribution is the ability to perform SAR on handheld devices twisted by their users along unknown paths. Ubicarse is not limited to localizing RF devices; it combines RF localization with stereo-vision algorithms to localize common objects with no RF source attached to them. We implement Ubicarse on a HP SplitX2 tablet and empirically demonstrate a median error of 39 cm in 3-D device localization and 17 cm in object geotagging in complex indoor settings. Indoor positioning systems have received increasing attention for supporting location-based services in indoor environments. WiFi-based indoor localization has been attractive due to its open access and low cost properties. However, the distance estimation based on received signal strength indicator (RSSI) is easily affected by the temporal and spatial variance due to the multipath effect, which contributes to most of the estimation errors in current systems. How to eliminate such effect so as to enhance the indoor localization performance is a big challenge. In this work, we analyze this effect across the physical layer and account for the undesirable RSSI readings being reported. We explore the frequency diversity of the subcarriers in OFDM systems and propose a novel approach called FILA, which leverages the channel state information (CSI) to alleviate multipath effect at the receiver. We implement the FILA system on commercial 802.11 NICs, and then evaluate its performance in different typical indoor scenarios. The experimental results show that the accuracy and latency of distance calculation can be significantly enhanced by using CSI. Moreover, FILA can significantly improve the localization accuracy compared with the corresponding RSSI approach. The recent growing interest for indoor Location-Based Services (LBSs) has created a need for more accurate and real-time indoor positioning solutions. The sparse nature of location finding makes the theory of Compressive Sensing (CS) desirable for accurate indoor positioning using Received Signal Strength (RSS) from Wireless Local Area Network (WLAN) Access Points (APs). We propose an accurate RSS-based indoor positioning system using the theory of compressive sensing, which is a method to recover sparse signals from a small number of noisy measurements by solving an 1-minimization problem. Our location estimator consists of a coarse localizer, where the RSS is compared to a number of clusters to detect in which cluster the node is located, followed by a fine localization step, using the theory of compressive sensing, to further refine the location estimation. We have investigated different coarse localization schemes and AP selection approaches to increase the accuracy. We also show that the CS theory can be used to reconstruct the RSS radio map from measurements at only a small number of fingerprints, reducing the number of measurements significantly. We have implemented the proposed system on a WiFi-integrated mobile device and have evaluated the performance. Experimental results indicate that the proposed system leads to substantial improvement on localization accuracy and complexity over the widely used traditional fingerprinting methods. As sensor-rich mobile devices became a commodity, more opportunities appeared for the creation of location-aware services. While GPS is a well established solution for outdoor localization, there is still no standard solution for localization indoors. This paper presents a novel accurate indoor positioning mechanism that is meant to run in common smartphones to be a readily and widely available solution. The system is based on multiple gait-model based filtering techniques for accurate movement quantification in combination with an advanced fused positioning mechanism that leverages sequences of opportunistic observations towards an accurate localization process. Magnetic field fluctuations, Wi-Fi readings and movement data are incrementally matched with a feature spot map containing multi-dimensional spatially-related features that characterize the building. A novel and convenient way of mapping the architectural and environmental properties of buildings is also introduced, which avoids the burden normally associated with the process. The system has been evaluated by multiple users in open and crowded spaces where overall median localization errors between 1.11 m and 1.68 m were obtained. While the reported errors are already satisfactory in the context of indoor localization, improvements may be readily achieved through the inclusion of additional reference features. High accuracy performance coupled with an opportunistic and infrastructure-free approach creates a very desirable solution for the indoor localization market doge.