Counter-Hypothetical Particle Filters for Single Object Pose Tracking

Particle filtering is a common technique for six degree of freedom (6D) pose estimation due to its ability to tractably represent belief over object pose. However, the particle filter is prone to particle deprivation due to the high-dimensional nature of 6D pose. When particle deprivation occurs, it can cause mode collapse of the underlying belief distribution during importance sampling. If the region surrounding the true state suffers from mode collapse, recovering its belief is challenging since the area is no longer represented in the probability mass formed by the particles. Previous methods mitigate this problem by randomizing and resetting particles in the belief distribution, but determining the frequency of reinvigoration has relied on hand-tuning abstract heuristics. In this paper, we estimate the necessary reinvigoration rate at each time step by introducing a Counter-Hypothetical likelihood function, which is used alongside the standard likelihood. Inspired by the notions of plausibility and implausibility from Evidential Reasoning, the addition of our Counter-Hypothetical likelihood function assigns a level of doubt to each particle. The competing cumulative values of confidence and doubt across the particle set are used to estimate the level of failure within the filter, in order to determine the portion of particles to be reinvigorated. We demonstrate the effectiveness of our method on the rigid body object 6D pose tracking task.Comment: International Conference on Robotics and Automation (ICRA) 202

DS-K3DOM: 3-D Dynamic Occupancy Mapping with Kernel Inference and Dempster-Shafer Evidential Theory

Occupancy mapping has been widely utilized to represent the surroundings for autonomous robots to perform tasks such as navigation and manipulation. While occupancy mapping in 2-D environments has been well-studied, there have been few approaches suitable for 3-D dynamic occupancy mapping which is essential for aerial robots. This paper presents a novel 3-D dynamic occupancy mapping algorithm called DSK3DOM. We first establish a Bayesian method to sequentially update occupancy maps for a stream of measurements based on the random finite set theory. Then, we approximate it with particles in the Dempster-Shafer domain to enable real time computation. Moreover, the algorithm applies kernel based inference with Dirichlet basic belief assignment to enable dense mapping from sparse measurements. The efficacy of the proposed algorithm is demonstrated through simulations and real experiments.Comment: 7 pages, 2 figures, submitted to ICRA 202

Traffic Scene Perception for Automated Driving with Top-View Grid Maps

Ein automatisiertes Fahrzeug muss sichere, sinnvolle und schnelle Entscheidungen auf Basis seiner Umgebung treffen. Dies benötigt ein genaues und recheneffizientes Modell der Verkehrsumgebung. Mit diesem Umfeldmodell sollen Messungen verschiedener Sensoren fusioniert, gefiltert und nachfolgenden Teilsysteme als kompakte, aber aussagekräftige Information bereitgestellt werden. Diese Arbeit befasst sich mit der Modellierung der Verkehrsszene auf Basis von Top-View Grid Maps. Im Vergleich zu anderen Umfeldmodellen ermöglichen sie eine frühe Fusion von Distanzmessungen aus verschiedenen Quellen mit geringem Rechenaufwand sowie eine explizite Modellierung von Freiraum. Nach der Vorstellung eines Verfahrens zur Bodenoberflächenschätzung, das die Grundlage der Top-View Modellierung darstellt, werden Methoden zur Belegungs- und Elevationskartierung für Grid Maps auf Basis von mehreren, verrauschten, teilweise widersprüchlichen oder fehlenden Distanzmessungen behandelt. Auf der resultierenden, sensorunabhängigen Repräsentation werden anschließend Modelle zur Detektion von Verkehrsteilnehmern sowie zur Schätzung von Szenenfluss, Odometrie und Tracking-Merkmalen untersucht. Untersuchungen auf öffentlich verfügbaren Datensätzen und einem Realfahrzeug zeigen, dass Top-View Grid Maps durch on-board LiDAR Sensorik geschätzt und verlässlich sicherheitskritische Umgebungsinformationen wie Beobachtbarkeit und Befahrbarkeit abgeleitet werden können. Schließlich werden Verkehrsteilnehmer als orientierte Bounding Boxen mit semantischen Klassen, Geschwindigkeiten und Tracking-Merkmalen aus einem gemeinsamen Modell zur Objektdetektion und Flussschätzung auf Basis der Top-View Grid Maps bestimmt

Belief Scheduler based on model failure detection in the TBM framework. Application to human activity recognition.

International audienceA tool called Belief Scheduler is proposed for state sequence recognition in the Transferable Belief Model (TBM) framework. This tool makes noisy temporal belief functions smoother using a Temporal Evidential Filter (TEF). The Belief Scheduler makes belief on states smoother, separates the states (assumed to be true or false) and synchronizes them in order to infer the sequence. A criterion is also provided to assess the appropriateness between observed belief functions and a given sequence model. This criterion is based on the conflict information appearing explicitly in the TBM when combining observed belief functions with predictions. The Belief Scheduler is part of a generic architecture developed for on-line and automatic human action and activity recognition in videos of athletics taken with a moving camera. In experiments, the system is assessed on a database composed of 69 real athletics video sequences. The goal is to automatically recognize running, jumping, falling and standing-up actions as well as high jump, pole vault, triple jump and {long jump activities of an athlete. A comparison with Hidden Markov Models for video classification is also provided

Learned Enrichment of Top-View Grid Maps Improves Object Detection

We propose an object detector for top-view grid maps which is additionally trained to generate an enriched version of its input. Our goal in the joint model is to improve generalization by regularizing towards structural knowledge in form of a map fused from multiple adjacent range sensor measurements. This training data can be generated in an automatic fashion, thus does not require manual annotations. We present an evidential framework to generate training data, investigate different model architectures and show that predicting enriched inputs as an additional task can improve object detection performance.Comment: 6 pages, 6 figures, 4 table

Belief Functions: Theory and Algorithms

The subject of this thesis is belief function theory and its application in different contexts. Belief function theory can be interpreted as a generalization of Bayesian probability theory and makes it possible to distinguish between different types of uncertainty. In this thesis, applications of belief function theory are explored both on a theoretical and on an algorithmic level. The problem of exponential complexity associated with belief function inference is addressed in this thesis by showing how efficient algorithms can be developed based on Monte-Carlo approximations and exploitation of independence. The effectiveness of these algorithms is demonstrated in applications to particle filtering, simultaneous localization and mapping, and active classification

Deep Learning-Based Machinery Fault Diagnostics

This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis

Towards a Taxonomic Benchmarking Framework for Predictive Maintenance: The Case of NASA’s Turbofan Degradation

The availability of datasets for analytical solution development is a common bottleneck in data-driven predictive maintenance. Therefore, novel solutions are mostly based on synthetic benchmarking examples, such as NASA’s C-MAPSS datasets, where researchers from various disciplines like artificial intelligence and statistics apply and test their methodical approaches. The majority of studies, however, only evaluate the overall solution against a final prediction score, where we argue that a more fine-grained consideration is required distinguishing between detailed method components to measure their particular impact along the prognostic development process. To address this issue, we first conduct a literature review resulting in more than one hundred studies using the C-MAPSS datasets. Subsequently, we apply a taxonomy approach to receive dimensions and characteristics that decompose complex analytical solutions into more manageable components. The result is a first draft of a systematic benchmarking framework as a more comparable basis for future development and evaluation purposes