### A random finite set approach for dynamic occupancy grid maps

**Zitiere als: **Nuss, Dominik (2017): A random finite set approach for dynamic occupancy grid maps. Open Access Repositorium der Universität Ulm. Dissertation. http://dx.doi.org/10.18725/OPARU-4361

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**Autor(en)**

Nuss, Dominik

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**Gutachter**

Dietmayer, KlausKoch, Wolfgang

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**Fakultät**

Fakultät für Ingenieurwissenschaften, Informatik und Psychologie#####
**Institution**

Institut für Mess-, Regel- und Mikrotechnik#####
**Ressourcen- / Medientyp**

Dissertation, Text#####
**Datum der Erstveröffentlichung**

2017-06-01#####
**Zusammenfassung**

Reliable vehicle environment perception is a basic prerequisite for advanced driver
assistant systems and autonomously driving cars. A common environment representation
form is an occupancy grid map. It divides the environment into single grid
cells and estimates for each cell whether the space it represents is occupied or free,
assuming grid cells are independent of each other. The mathematical framework is
based on the binary Bayes filter (BBF), which combines sensor measurements from
different sensors and potentially from different points in time. Since an occupancy
grid map does not employ a concept of individual objects, it is able to represent
arbitrarily shaped obstacles.
A classical occupancy grid map is not eligible for estimating dynamic environments,
because it does not apply a process model. A much-noticed extension to a static
occupancy grid map is the Bayesian occupancy filter (BOF). In contrast to a classical
occupancy grid, the BOF estimates a velocity distribution for the occupancy of each
grid cell based on a histogram filter. Since the BOF is computationally extremely
demanding, recent publications suggest to represent the dynamic state of grid cells
with particles. This allows to calculate dynamic grid maps in real-time applications
with increased grid cell size and resolution. Today, dynamic occupancy grid maps are
still a younger research area and not as well-studied as object-tracking approaches
are. Up to now, the BOF has been addressed as a research field with little connection
to other tracking methods.
This work presents a new concept of dynamic grid mapping as an approximation
of a random finite set (RFS) filter. A random finite set is a general, probabilistic
representation of a random but limited number of objects and their states. The finite
set statistics (FISST) describe Bayesian filtering of random finite sets and are basis
for a number of multi-object tracking approaches like the probability hypothesis
density (PHD) filter. Describing the grid as a random finite set allows transferring
advanced concepts from the well-established field of random finite set filtering to
the field of dynamic grid mapping. The thesis derives a filter called probability
hypothesis density / multi-instance Bernoulli (PHD/MIB) filter, which represents
and propagates the dynamic grid map in alternating forms as a PHD and as multiple
instances of Bernoulli filters.
Additionally, the thesis presents a sequential Monte Carlo (SMC) realization of
the PHD/MIB filter and an approximation in the Dempster-Shafer domain called
Dempster-Shafer PHD/MIB (DS-PHD/MIB) filter, which requires a smaller number
of particles than the original PHD/MIB filter. The thesis describes in detail an
efficient, massively parallel implementation of the DS-PHD/MIB filter and outlines
the algorithm in pseudo code.
Finally, the thesis describes characteristics of the DS-PHD/MIB filter and discusses
its advantages and disadvantages compared to object-based tracking approaches
using practical application examples. A quantitative evaluation with real-world data
shows that the DS-PHD/MIB filter provides consistent state estimation results and
that it appropriately models the stochastic multi-object transition process and the
stochastic multi-object observation process. Furthermore, the evaluation confirms
the real-time capability of the parallelized implementation of the DS-PHD/MIB
filter and its usefulness for state estimation of a dynamic vehicle environment. A short version of the thesis has been submitted for publication in
The International Journal of Robotics Research and has been made available to the public via arXiv:
Nuss, Dominik; Reuter, Stephan; Thom, Markus; Yuan, Ting; Krehl, Gunther; Maile,
Michael; Gern, Axel; Dietmayer, Klaus: A random finite set approach for dynamic
occupancy grid maps with real-time application. In: ArXiv e-prints, 2016. Available
online at http://arxiv.org/abs/1605.02406.

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**LCSH**

RoboticsAutonomous vehicles

Driver assistance systems

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**GND**

RobotikObjektverfolgung

Autonomes Fahrzeug

Fahrerassistenzsystem

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**Freie Schlagwörter**

Self-driving carsObject tracking

Sensor data fusion

Random finite sets

Environment perception