Thesis (MEng)--Stellenbosch University, 2015. / ENGLISH ABSTRACT: This thesis investigates the use of stereo vision sensors for dense autonomous mapping. It characterises
and analyses the errors made during the stereo matching process so measurements can be correctly
integrated into a 3D grid-based map. Maps are required for navigation and obstacle avoidance on
autonomous vehicles in complex, unknown environments. The safety of the vehicle as well as the public
depends on an accurate mapping of the environment of the vehicle, which can be problematic when
inaccurate sensors such as stereo vision are used. Stereo vision sensors are relatively cheap and convenient,
however, and a system that can create reliable maps using them would be beneficial.
A literature review suggests that occupancy grid mapping poses an appropriate solution, offering
dense maps that can be extended with additional measurements incrementally. It forms a grid representation
of the environment by dividing it into cells, and assigns a probability to each cell of being occupied.
These probabilities are updated with measurements using a sensor model that relates measurements to
occupancy probabilities.
Numerous forms of these sensor models exist, but none of them appear to be based on meaningful
assumptions and sound statistical principles. Furthermore, they all seem to be limited by an assumption
of unimodal, zero-mean Gaussian measurement noise.
Therefore, we derive a principled inverse sensor model (PRISM) based on physically meaningful
assumptions. This model is capable of approximating any realistic measurement error distribution using a
Gaussian mixture model (GMM). Training a GMM requires a characterisation of the measurement errors,
which are related to the environment as well as which stereo matching technique is used. Therefore, a
method for fitting a GMM to the error distribution of a sensor using measurements and ground truth is
presented.
Since we may consider the derived principled inverse sensor model to be theoretically correct under
its assumptions, we use it to evaluate the approximations made by other models from the literature
that are designed for execution speed. We show that at close range these models generally offer good
approximations that worsen with an increase in measurement distance.
We test our model by creating maps using synthetic and real world data. Comparing its results to
those of sensor models from the literature suggests that our model calculates occupancy probabilities
reliably. Since our model captures the limited measurement range of stereo vision, we conclude that
more accurate sensors are required for mapping at greater distances. / AFRIKAANSE OPSOMMING: Hierdie tesis ondersoek die gebruik van stereovisie sensors vir digte outonome kartering. Dit karakteriseer
en ontleed die foute wat gemaak word tydens die stereopassingsproses sodat metings korrek geïntegreer
kan word in 'n 3D rooster-gebaseerde kaart. Sulke kaarte is nodig vir die navigasie en hindernisvermyding
van outonome voertuie in komplekse en onbekende omgewings. Die veiligheid van die voertuig sowel as
die publiek hang af van 'n akkurate kartering van die voertuig se omgewing, wat problematies kan wees
wanneer onakkurate sensors soos stereovisie gebruik word. Hierdie sensors is egter relatief goedkoop en
gerieflik, en daarom behoort 'n stelsel wat hulle dit gebruik om op 'n betroubare manier kaarte te skep
baie voordelig te wees.
'n Literatuuroorsig dui daarop dat die besettingsroosteralgoritme 'n geskikte oplossing bied, aangesien
dit digte kaarte skep wat met bykomende metings uitgebrei kan word. Hierdie algoritme skep
'n roostervoorstelling van die omgewing en ken 'n waarskynlikheid dat dit beset is aan elke sel in die
voorstelling toe. Hierdie waarskynlikhede word deur nuwe metings opgedateer deur gebruik te maak van
'n sensormodel wat beskryf hoe metings verband hou met besettingswaarskynlikhede.
Menigde a
eidings bestaan vir hierdie sensormodelle, maar dit blyk dat geen van die modelle gebaseer
is op betekenisvolle aannames en statistiese beginsels nie. Verder lyk dit asof elkeen beperk word deur
'n aanname van enkelmodale, nul-gemiddelde Gaussiese metingsgeraas.
Ons lei 'n beginselfundeerde omgekeerde sensormodel af wat gebaseer is op fisies betekenisvolle aannames.
Hierdie model is in staat om enige realistiese foutverspreiding te weerspieël deur die gebruik van
'n Gaussiese mengselmodel (GMM). Dit vereis 'n karakterisering van 'n stereovisie sensor se metingsfoute,
wat afhang van die omgewing sowel as watter stereopassingstegniek gebruik is. Daarom stel ons
'n metode voor wat die foutverspreiding van die sensor met behulp van 'n GMM modelleer deur gebruik
te maak van metings en absolute verwysings.
Die afgeleide ge inverteerde sensormodel is teoreties korrek en kan gevolglik gebruik word om modelle
uit die literatuur wat vir uitvoerspoed ontwerp is te evalueer. Ons wys dat op kort afstande die modelle
oor die algemeen goeie benaderings bied wat versleg soos die metingsafstand toeneem.
Ons toets ons nuwe model deur kaarte te skep met gesimuleerde data, sintetiese data, en werklike data.
Vergelykings tussen hierdie resultate en dié van sensormodelle uit die literatuur dui daarop dat ons model
besettingswaarskynlikhede betroubaar bereken. Aangesien ons model die beperkte metingsafstand van
stereovisie vasvang, lei ons af dat meer akkurate sensors benodig word vir kartering oor groter afstande.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/96925 |
| Date | 03 1900 |
| Creators | Burger, Alwyn Johannes |
| Contributors | Van Daalen, Corne E., Brink, Willem H., Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering. |
| Publisher | Stellenbosch : Stellenbosch University |
| Source Sets | South African National ETD Portal |
| Language | en_ZA |
| Detected Language | Unknown |
| Type | Thesis |
| Format | 95 pages : illustrations |
| Rights | Stellenbosch University |
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