A large fraction of our knowledge about the real world
is concerned with the temporal domain; we learn to under-
stand this during early life more or less subconsciously
while the capability of crawling, walking and manipulat-
ing other objects under earth gravity is being acquired. The
temporal sequence of states of moving objects and their
transition characteristics constitute very essential knowl-
edge about the real world providing us with the capability
of acting adequately even though it does not seem to be
represented explicitly. This has long been overlooked in
Artificial Intelligence which concentrated its efforts on
explicitly represented abstract knowledge about quasi-
static relations between objects in the world.
The natural sciences and engineering technology have
developed adequate methods for representing these facts
about the physical world. They describe them within the
framework of differential equations with time as mono-
tonically increasing independeni variable. As I.Kant has
elaborated in his 'Critiques ...' more than two centuries
ago it has to be kept in mind that space and time are not
properties of objects. We cannot help carrying it into the
world by our sensing and analysis systems; we ourselves
exist in these basic four dimensions. Therefore, it was
decided to install these basic four dimensions in the 4D-
approach to dynamic machine vision right from the begin-
ning in order to be able to deal with the real world
efficiently. This was the main contribution of our approach
to machine vision; the rest follows almost automatically.
2. System components
The availability of two different testbeds each with a
navigation system based on the 4D-approach [Dickmanns,
Graefe, 88], allows test runs to be performed under various
Fig.1: Experimental vehicles: a) VaMoRs b) ATHENE
kinds of circumstances. The first vehicle is clearly
specified to indoor applications and called 'ATHENE',
whereas the second one, dubbed ' VaMoRs', is designed
for outdoor usage. Each system has its specific advan-
tages, but the overall design of the navigation system is
closely related, so there are no difficulties to transfer well
proven solutions between the two. Since ' VaMoRs' is well
documented in [Zapp 88; Dickmanns, Graefe 88] more
effort is put on describing details of 'ATHENE' in this
paper.
The main components of the indoor experimental setup
can be divided into three categories. First, the robot itself,
which is a converted AGV equipped with all necessary
actuators, interfaces and onboard power supply. Second,
a special multiprocessor vision system for realtime image
sequence analysis and interpretation is placed on the ve-
hicle, and third, there are two extra computers for the
navigational task and the low level control system.
ATHENE can be driven autonomously under computer
control and serves as a rolling indoor platform for research
work on landmark navigation and computer vision.
In the front part of the vehicle an electromechanical pan
platform is mounted carrying a standard monochrome
CCD camera. Viewing direction control is done either by
the navigation system or the vision system itself, depend-
ing on the actual task. The camera pointing capability
allows active scene search and horizontal tracking, e.g. for
initial self orientation or landmark tracking while driving.
For image sequence processing a custom made system
BVV 2 [Graefe 90] with four Intel 80286 processors has
been utilized in the experiments. This multiprocessor sys-
tem of the MIMD type consists (in the case of the indoor
application) of 4 commercial, standard Multibus I single-
board computers spanning the performance range from
8086 to 80286. Key feature of this multiprocessor vision-
system is the physically distributed, thus truly parallel
image access capability of all CPUs directly involved in
image operations. This overcomes the common I/O bot-
tleneck of general purpose machines, in which usually
only one processor has direct image access. Here, no
central frame store exists. Any processor linked to the
videobus through a custom made videobus-interface
(VBI) can simultaneously access and process a subseg-
ment (window) of the digitized 256 by 244x8 bit per pel
grayscale image. The VBI basically is a hardware-attach-
ment to a standard singleboard computer containing a
window-selection logic and two fast window-buffers stor-
ing 4k pel each. Multiple windows can be independently
positioned or changed in size, shape and sampling density
under software control. It should be noted that except for
the VBI no custom hardware and no dedicated image
processing devices are being used in this experimental
system. The advantages of applying easily programmable
standard microprocessors instead, proved to be significant
for the system's applicability and efficiency as a research
tool. A further key point is the flexible interprocessor
communication scheme based on message passing, form-