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.
Meanwhile, beacon allocation traditionally relies on coverage as a heuristic to guide placement in a specific environment. When the environment is polygonal and beacons have an unlimited field-of-view subject to occlusions by walls (e.g., cameras), coverage corresponds to the standard art-gallery visibility problem @cite_3 . Related, propose a greedy landmark-based method that solves for the placement of the minimum number of beacons (within a @math factor) necessary to cover all but a fraction of a given polygonal environment. However, a beacon's sensing geometry is often complex as a result of partial attenuation due to the environment, beacon noise, and signal interference. Interference varies according to the placement of other beacons and channel allocation, which most coverage algorithms do not reason over. These factors are particularly important for RF-based localization, which is sensitive to the effects of aliasing, further complicating the relationship between allocation and inference.
@cite_3
2099719044
Beacon placement strongly affects the quality of spatial localization, a critical service for context-aware applications in wireless sensor networks; yet this aspect of localization has received little attention. Fixed beacon placement approaches such as uniform and very dense placement are not always viable and will be inadequate in very noisy environments in which sensor networks may be expected to operate (with high terrain and propagation uncertainties). We motivate the need for empirically adaptive beacon placement and outline a general approach based on exploration and instrumentation of the terrain conditions by a mobile human or robot agent. We design, evaluate and analyze three novel adaptive beacon placement algorithms using this approach for localization based on RF-proximity. In our evaluation, we find that beacon density rather than noise level has a more significant impact on beacon placement algorithms. Our beacon placement algorithms are applicable to a low (beacon) density regime of operation. Noise makes moderate density regimes more improvable.