Susanna Kaiser

Comparison of Error Concealment Techniques for an MPEG-2 Video Decoder in Terrestrial TV-Broadcasting

In terrestrial broadcasting the received power fluctuation might be in the order of 10 dB, where a non-hierarchical TV-transmission scheme may fail at bad reception conditions. A two layer hierarchical transmission system, also specified by the European digital terrestrial TV-broadcasting standard, may help to have better reception conditions especially for fixed receivers. However, the portable/mobile receiver that only needs the base layer of this hierarchical signal may risk a total loss of picture. Since no time interleaving is foreseen in the standard, the situation is more critical in the case of shadowing, high Doppler (mobile reception), or impulsive noise, whereas even for fixed receivers with two hierarchical levels a good quality of service cannot be guaranteed. Therefore, in order to assume a continuous service with higher quality, some post-processing techniques such as error concealment at the receiver side will be desirable. These techniques, for instance even in the case of hierarchical transmission, may offer better results by concealing the errors instead of using only the most robust stream. The aim of this article is to study different error concealment techniques for the European digital terrestrial TV-broadcasting standard where the source coding is based on MPEG-2. These techniques are based on temporal, spatial and frequency error concealment techniques and on the combination of them. Simulations showed that motion compensated temporal error concealment provides best results for P- and B-pictures, whereas a combination of temporal error concealment and re-synchronization or the use of shorter slices provides best results for I-pictures.

A pedestrian navigation system using a map-based angular motion model for indoor and outdoor environments

By incorporating known floor-plans in sequential Bayesian positioning estimators, such as particle filters, long-term positioning accuracy can be achieved as long as the map is sufficiently accurate and the environment sufficiently constrains pedestrians’ motion. Instead of using binary decisions to eliminate particles when crossing a wall, map-based angular probability density functions (PDFs) are used in this article that are capable of weighting the possible headings of the pedestrian according to local infrastructure. In addition, we will include outdoor maps by processing satellite images of the region. We will show that the angular PDFs will help to obtain better performance in critical multi-modal navigation scenarios and in the outdoor area when including maps.

Performance comparison of foot- and pocket-mounted inertial navigation systems

The aim of this paper is to compare two inertial navigation systems in order to identify their strengths and weaknesses as well as to propose new fusion algorithms for these systems. The goal of the fusion is to combine the best of both navigation systems in order to obtain an improved position estimation of the pedestrian. To that extent, the comparison starts with an analysis of the sensor parameters obtained with the Allan variance method. The comparison continues with the analysis of the odometries obtained with both pocket- and foot-mounted sensors. This is done by comparing the true positions of a set of ground truth points with the position estimated by the navigation systems. The analysis shows that the pocket-mounted system is reliable for step detection, whereas the foot-mounted system misses some stance phase periods. The advantage of the foot-mounted system is, for example, the good estimation of the distance. For completeness and based on the results, we propose several fusion techniques to improve the pedestrians position estimation.

Maps-based angular PDFs used as prior maps for FootSLAM

FootSLAM (Simultaneous Localization and Mapping) is a new technology that addresses the indoor mapping and positioning challenge that relies only on sensors that the person carries. In this paper, we propose to use maps-based angular Probability Density Functions (PDFs), using prior knowledge of the building layout as prior maps for FootSLAM and show how they can be integrated into the FootSLAM weight update. The angular PDFs - that represent PDFs for a human step direction at a given location - are derived from a diffusion algorithm based on maps. The advantage of using prior maps is that the FootSLAM algorithm reaches convergence faster and more accurately.

Measuring the Uncertainty of Probabilistic Maps Representing Human Motion for Indoor Navigation

Indoor navigation and mapping have recently become an important field of interest for researchers because global navigation satellite systems (GNSS) are very often unavailable inside buildings. FootSLAM, a SLAM (Simultaneous Localization and Mapping) algorithm for pedestrians based on step measurements, addresses the indoor mapping and positioning problem and can provide accurate positioning in many structured indoor environments. In this paper, we investigate how to compare FootSLAM maps via two entropy metrics. Since collaborative FootSLAM requires the alignment and combination of several individual FootSLAM maps, we also investigate measures that help to align maps that partially overlap. We distinguish between the map entropy conditioned on the sequence of pedestrian’s poses, which is a measure of the uncertainty of the estimated map, and the entropy rate of the pedestrian’s steps conditioned on the history of poses and conditioned on the estimated map. Because FootSLAM maps are built on a hexagon grid, the entropy and relative entropy metrics are derived for the special case of hexagonal transition maps. The entropy gives us a new insight on the performance of FootSLAM’s map estimation process.

PocketSLAM based on the principle of the FootSLAM algorithm

Infrastructureless indoor navigation remains a challenging research area in spite of the fact that multiple low cost sensors suitable for positioning recently became available even in mobile phones. Since Global Navigation Satellite Signals (GNSS) are often unavailable indoors, the use of Inertial Measurement Units (IMUs) seems to be promising for indoor navigation. Auspicious results are available when the sensor is placed on the foot in indoor positioning applications, but still many problems remain when the drift error during stance phases cannot be reduced. An indoor navigation solution is the so called FootSLAM algorithm, where reliable positioning (positioning error below 1 meter in the case of loop closures) is possible assuming that the sensor is fixed on the foot. More recently, another navigation system was proposed where the sensor is located in the pocket of a pedestrian. Since no special fixation or no special shoe is needed, this location of the sensor is more convenient for mass market users. But also this system suffers from a remaining drift that needs to be compensated. In this paper, we propose a combination of the pocket navigation system with the FootSLAM approach and show that it is also possible to reduce the remaining drift with this combination.

Mobile TV - "Daumenkino" gets Reality

Mobile TV started almost 30 years ago as a research project, but only just recently TV-like services for mobile users on handheld devices have become reality. In this work, we want to give an overview of both the history and current advances in this highly active field of research, including unicast and broadcast media streaming over wireless channels and efficient forward error correction (FEC) mechanisms at the application layer.

Height Error Correction for Shoe-Mounted Inertial Sensors Exploiting Foot Dynamics

Shoe-mounted inertial sensors are widespread deployed in satellite-denied scenarios because of the possibility to re-calibrate the estimated position stepwise. These re-calibrations, known as zero-velocity corrections, prevent an accumulated positioning error growth over time caused by the noise of current medium- and low-cost sensors. However, the error accumulated over time in the height estimation is still an issue under study. The objective of this article is to propose a height correction that is based on the dynamics of the foot. The presented algorithm analyzes the movement of the foot, which is different when walking on horizontal surfaces and stairs. The identification of horizontal surfaces and stairs is detailed in this article. For the assessment of the performance of the proposed height correction, a dataset of approximately 5 h recorded with 10 volunteers walking in a five-story building is employed. The error is evaluated using pre-defined ground truth points. We compare the height error estimated with and without applying the proposed correction and show that the height correction improves the vertical positioning accuracy up to 85%.

Integrating known locations in FootSLAM and investigating the influence of different prior information

Pedestrian positioning is still a challenging field of interest when not receiving any GNSS or other reference signals as it might be the case in indoor environments or tunnels. For professional applications, where it is not possible to rely on any infrastructure, a common technique is to mount Inertial Measurement Units (IMUs) on the foot or other parts of the body for positioning. IMU based techniques still suffer from the remaining drift especially when the environment is unknown and not re-visited, or when the pedestrian walks randomly in large areas. In order to overcome this problem the so called FootSLAM algorithm applied in this paper is extended to handle known locations. FootSLAM is a SLAM (Simultaneous Localization and Mapping) algorithm estimating the map of the environment while walking wearing an inertial sensor on the foot or at other locations of the body. With only few known locations the estimated FootSLAM map can be refined or corrected in the case of non-convergence in critical areas. Beside the derivation of the algorithm handling known locations in FootSLAM, the influence of different kinds of prior information on the FootSLAM algorithm is analyzed in this paper in terms of error and map quality performance.