Segmentation of floating objects in LIDAR data

Mirčeta, Kristijan (author), Marolt, Matija (mentor), Bohak, Ciril (co-mentor)

In this thesis we attempt to segment floating objects – for example, airplanes, birds, balloons, and drones – in airborne LiDAR data. We present a heuristic method (RBNN) for discovering floating objects and evaluate it on a subset of the ARSO dataset, which is a scan of the entire terrain of Slovenia. The results offer many candidates but only one true floating object in 3 square kilometers of terrain. We conclude that there are too few positives to solve this problem via supervised learning. To address this issue, we augment the dataset with virtual floating objects and then train a machine learning model to find these in the augmented dataset. The idea is that such a model will then be able to find similar objects in unseen data that are in fact real floating objects. We present an augmentation pipeline with which we can augment any LiDAR dataset with any polygonal model (known as an augmentable). We determine the quality of the augmentation by evaluating the consistency of the virtual scanning with the real scanning. This is composed of two subproblems: where to place the augmentable and how to scan it. Our method reliably solves the problem of placing the augmentable. The difficulty in scanning lies in determining the scan direction and the location of the scanner during scanning. We determine the direction with 18 angular degrees of error, computed on 2,219 augmentables, over 50 square kilometers of ARSO dataset terrain, and we can reliably determine the location of the scanner if we know its direction. In the end, we use a modern point cloud segmentation method with the augmented data. The algorithm has advantages and disadvantages when compared to RBNN, and it may be beneficial to combine both methods.

• Language: English
• Keywords: computer vision, machine perception, segmentation, point cloud segmentation, airborne LiDAR simulation, LiDAR augmentation
• Work type: Master's thesis/paper
• Organization: FRI - Faculty of Computer and Information Science
• Year: 2019
• PID: 20.500.12556/RUL-113243
• COBISS.SI-ID: 1538500291
• Publication date in RUL: 16.12.2019

Secondary language

• Language: Slovenian
• Title: Segmentacija lebdečih predmetov v podatkih LIDAR

Abstract

V nalogi poskusimo segmentirati lebdeče predmete, kot na primer letala, ptice, balone ali drone, v zračnih podatkih LiDAR. Predstavimo hevristično metodo za odkrivanje lebdečih predmetov (RBNN). Preizkusimo jo na delu podatkovnega nabora ARSO, ki je zajem terena celotne Slovenije s tehnologijo LiDAR. Rezultati ponudijo precej kandidatov, vendar po pregledu najdemo le en lebdeči predmet med kandidati v treh kvadratnih kilometrih terena. Sklenemo, da podatkovni nabor ne vsebuje dovolj lebdečih predmetov za rešitev problema s strojnim učenjem. Poskusimo z obogatenjem podatkovne množice z navideznimi lebdečimi objekti, nato pa učenjem modela strojnega učenja iskati le te v obogatenem podatkovnem naboru. Ideja je, da bo tako naučen model našel podobne predmete, ki so v resnici pravi lebdeči predmeti. Predstavimo cevovod obogatitve, s katerim obogatimo podatkovni nabor LiDAR s poljubnim poligonskim modelom (dodan predmet). Kvaliteto obogatitve vrednotimo po konsistentnosti skeniranja dodanih predmetov z realnim skeniranjem LiDAR. To sestoji iz vprašanj kam naj dodani predmet postavimo in kako naj ga posnamemo. Naša metoda zanesljivo reši problem postavljanja dodanega predmeta. Problem snemanja je določanje smeri in lokacije snemalnika. Smer z našo metodo izračunamo s povprečnih 18 kotnih stopinj napake, izračunanih na 2,219 dodanih predmetih, na 50 kvadratnih kilometrih terena v podatkovnem naboru ARSO, ter zanesljivo izračunamo lokacijo snemalnika, če poznamo smer snemanja. Na koncu uporabimo sodobno metodo za segmentacijo oblakov točk z navedenimi obogatenimi podatki. Algoritem ima prednosti in slabosti v primerjavi z metodo RBBN, perspektivna ideja je njuna združitev.

• Keywords: računalniški vid, umetno zaznavanje, segmentacija, segmentacija oblakov točk, zračna simulacija LiDAR, obogatitev LiDAR