Philipp Vorst

Self-Localization with RFID snapshots in densely tagged environments

In this paper we show that, despite some disadvantageous properties of radio frequency identification (RFID), it is possible to localize a mobile robot quite accurately in environments which are densely tagged. We therefore employ a recently presented probabilistic fingerprinting technique called RFID snapshots. This method interprets short series of RFID measurements as feature vectors and is able to position a mobile robot after a training phase. It requires no explicit sensor model and is capable of exploiting given tag infrastructures, e.g., provided by supermarket shelves containing labeled products.

Semi-autonomous Learning of an RFID Sensor Model for Mobile Robot Self-localization

In this paper, we present a method of learning a probabilistic RFID reader model with a mobile robot in a semi-automatic fashion. RFID and position data, recorded during an exploration phase, are used to learn the probability of detecting an RFID tag, for which we investigate two non-parametric probability density estimation techniques. The trained model is finally used to localize the robot via a particle filter-based approach and optimized with respect to the resulting localization error. Experiments have shown that the learned models perform comparably well as a grid-based model learned from measurements in a stationary setup, but can be obtained easier.

Efficient self-adjusting, similarity-based location fingerprinting with passive UHF RFID

In this paper we present extensive experimental results of location fingerprinting with passive UHF radio-frequency identification (RFID). As recent passive RFID hardware provides information about received signal strength (RSS), we evaluate its usefulness in the context of fingerprinting based on classical vector similarity measures. We analyze the impact of decisive parameters of the applied approach and also select them automatically via cross-validation, including the most appropriate similarity measure. A further novelty is an RSS thresholding mechanism which reduces the computational demands of comparing fingerprints. This technique is especially useful in surroundings which are densely equipped with RFID tags, such as future supermarkets or logistic centers. We conducted real-world experiments with a mobile robot and two different RFID readers. Results are reported both for global localization in each time frame and for time-filtered position tracking. We provide the experimental data of this work for download.

Fully autonomous trajectory estimation with long-range passive RFID

We present a novel approach which enables a mobile robot to estimate its trajectory in an unknown environment with long-range passive radio-frequency identification (RFID). The estimation is based only on odometry and RFID measurements. The technique requires no prior observation model and makes no assumptions on the RFID setup. In particular, it is adaptive to the power level, the way the RFID antennas are mounted on the robot, and environmental characteristics, which have major impact on long-range RFID measurements. Tag positions need not be known in advance, and only the arbitrary, given infrastructure of RFID tags in the environment is utilized. By a series of experiments with a mobile robot, we show that trajectory estimation is achieved accurately and robustly.

Visual mapping with uncertainty for correspondence-free localization using Gaussian process regression

We present a framework that allows for localization based on very low resolution omnidirectional image data using regression techniques. Previous related methods are constrained to image data labeled with exact position information acquired in the training phase. We relax this constraint and propose to learn local heteroscedastic Gaussian processes by accumulating odometry data which can easily be acquired. The processes are used as a probabilistic map to predict recording positions of newly acquired images by a fusion of the uncertain training data. In contrast to many feature-based approaches, our framework does not rely on any explicit correspondences over images as well as over positions and only imposes very weak assumptions on the type and quality of the image representations.

Particle filter-based trajectory estimation with passive UHF RFID fingerprints in unknown environments

In this paper we present a novel approach to estimating the trajectory of a robot by means of inexpensive passive RFID tags and odometry in unknown environments. We show how trajectory estimation, a prerequisite of mapping RFID transponder positions without a reference positioning system, can be achieved using a particle filter. The presented technique is based on a non-parametric model of spatial relationships between RFID measurements. It overcomes the noisy nature of RFID measurements and the absence of distance and bearing information. The accuracy of our method is investigated in a series of experiments with a mobile robot.