text: The ability of robots to estimate their location is crucial for a wide variety of autonomous operations. In settings where GPS is unavailable, measurements of transmissions from fixed beacons provide an effective means of estimating a robot's location as it navigates. The accuracy of such a beacon-based localization system depends both on how beacons are distributed in the environment, and how the robot's location is inferred based on noisy and potentially ambiguous measurements. We propose an approach for making these design decisions automatically and without expert supervision, by explicitly searching for the placement and inference strategies that, together, are optimal for a given environment. Since this search is computationally expensive, our approach encodes beacon placement as a differential neural layer that interfaces with a neural network for inference. This formulation allows us to employ standard techniques for training neural networks to carry out the joint optimization. We evaluate this approach on a variety of environments and settings, and find that it is able to discover designs that enable high localization accuracy.
  @cite_8: Most autonomous underwater vehicle (AUV) systems rely on prior knowledge of beacon locations for localization. We present a system capable of navigating without prior beacon locations. Noise and outliers are major issues; we present a powerful outlier rejection method that imposes geometric constraints on measurements. We have successfully applied our algorithm to real-world data and have demonstrated navigation performance comparable to that of systems that assume known beacon locations.
  @cite_15: 
  @cite_13: Recently, indoor positioning systems (IPSs) have been designed to provide location information of persons and devices. The position information enables location-based protocols for user applications. Personal networks (PNs) are designed to meet the users' needs and interconnect users' devices equipped with different communications technologies in various places to form one network. Location-aware services need to be developed in PNs to offer flexible and adaptive personal services and improve the quality of lives. This paper gives a comprehensive survey of numerous IPSs, which include both commercial products and research-oriented solutions. Evaluation criteria are proposed for assessing these systems, namely security and privacy, cost, performance, robustness, complexity, user preferences, commercial availability, and limitations.We compare the existing IPSs and outline the trade-offs among these systems from the viewpoint of a user in a PN.
  @cite_2: This paper is about using range-only data to navigate an autonomous underwater vehicle (AUV). We assume the vehicle is equipped with conventional long base line (LBL) transceiver which measures acoustic time of flights (TOFs) between vehicle and small submerged transponders. Using only range data and no prior information other than approximate water column depth, we solve for both transponder location and vehicle trajectory. Results are given using data from a AUV operating in shallow water. A ground truth comparison is made with surveyed transponder locations and trajectory estimates from an on-board Doppler Compass LBL derived navigation filter.
summary: Most of the effort in localization is typically devoted to finding accurate inference methods, assuming the distribution and location of beacons in the environment are given. One setting for these methods is where sensor measurements can be assumed to provide direct, albeit possibly noisy, measurements of relative range or bearing from beacons---an assumption that is typically based on a simple model for signal propagation. Location estimation then proceeds by using these relative range and or bearing estimates and knowledge of beacon locations. For example, acoustic long baseline (LBL) networks are frequently used to localize underwater vehicles @cite_2 @cite_8 , while a number of low-cost systems exist that use RF and ultrasound to measure range @cite_13 @cite_15 .