Convolutional Cross-View Pose Estimation

We propose a novel end-to-end method for cross-view pose estimation. Given a ground-level query image and an aerial image that covers the query's local neighborhood, the 3 Degrees-of-Freedom camera pose of the query is estimated by matching its image descriptor to descriptors of local regions within the aerial image. The...

An Extended Kalman Filter for Magnetic Field SLAM Using Gaussian Process Regression

We present a computationally efficient algorithm for using variations in the ambient magnetic field to compensate for position drift in integrated odometry measurements (dead-reckoning estimates) through simultaneous localization and mapping (SLAM). When the magnetic field map is represented with a reduced-rank Gaussian process (GP) using...

Enabling Robust Radar-based Localization and Vital Signs Monitoring in Multipath Propagation Environments

Objective: Over the last two decades, radar-based contactless monitoring of vital signs (heartbeat and respiration rate) has raised increasing interest as an emerging and added value to health care. However, until now, the flaws caused by indoor multipath propagation formed a fundamental hurdle for the adoption of such technology in practical...

A Computationally Efficient Moving Horizon Estimator for Ultra-Wideband Localization on Small Quadrotors

We present a computationally efficient moving horizon estimator that allows for real-time localization using Ultra-Wideband measurements on small quadrotors. The estimator uses only a single iteration of a simple gradient descent method to optimize the state estimate based on past measurements, while using random sample consensus to reject...

A Local Particle Filter Using Gamma Test Theory for High-Dimensional State Spaces

Particle filters are non-Gaussian filters, which means that the assumption that the error distribution of the ensemble should be Gaussian is unnecessary. Like the ensemble Kalman filter, particle filters are based on the Monte Carlo approximation to represent the distribution of model states. It requires a substantial number of particles to...

Unsupervised Tuning of Filter Parameters Without Ground-Truth Applied to Aerial Robots

Autonomous robots heavily rely on well-tuned state estimation filters for successful control. This letter presents a novel automatic tuning strategy for learning filter parameters by minimizing the innovation, i.e., the discrepancy between expected and received signals from all sensors. The optimization process only requires the inputs and...

Network localization is unalterable by infections in bursts

To shed light on the disease localization phenomenon, we study a bursty susceptible-infected-susceptible (SIS) model and analyze the model under the mean-field approximation. In the bursty SIS model, the infected nodes infect all their neighbors periodically, and the near-threshold steady-state prevalence is non-constant and maximized by a...

Urban Objects Classification With an Experimental Acoustic Sensor Network

This paper proposes feature extraction methods for object classification with passive acoustic sensor networks deployed in suburban environments. We analyzed the emitted acoustic signals of three object classes: 1) guns (muzzle blast); 2) vehicles (running piston engine); and 3) pedestrians (several footsteps). Based on the conducted analysis,...

Targeting a heterologous protein to multiple plant organelles via rationally designed 5? mRNA tags

Background Plant bioengineers require simple genetic devices for predictable localization of heterologous proteins to multiple subcellular compartments. Results We designed novel hybrid signal sequences for multiple-compartment localization and characterize their function when fused to GFP in Nicotiana benthamiana leaf tissue. TriTag-1 and...

Ranging in an underwater medium with multiple isogradient sound speed profile layers

In this paper, we analyze the problem of acoustic ranging between sensor nodes in an underwater environment. The underwater medium is assumed to be composed of multiple isogradient sound speed profile (SSP) layers where in each layer the sound speed is linearly related to the depth. Furthermore, each sensor node is able to measure its depth and...

Ranging energy optimization for robust sensor positioning based on semidefinite programming

Sensor positioning is an important task of location-aware wireless sensor networks. In most sensor positioning systems, sensors and beacons need to emit ranging signals to each other. Sensor ranging energy should be low to prolong system lifetime, but sufficiently high to fulfill prescribed accuracy requirements. This motivates us to investigate...