Extraction of main levels of a building from a large point cloud

Horizontal levels are references entities, the base of man-made environments. Their creation is the first step for various applications including the BIM (Building Information Modelling). BIM is an emerging methodology, widely used for new constructions, and increasingly applied to existing buildings (scan-to-BIM). The as-built BIM process is still mainly manual or semi-automatic and therefore is highly time-consuming. The automation of the as-built BIM is a challenging topic among the research community. This study is part of an ongoing research into the scan-to-BIM process regarding the extraction of the principal structure of a building. More specifically, here we present a strategy to automatically detect the building levels from a large point cloud obtained with a terrestrial laser scanner survey. The identification of the horizontal planes is the first indispensable step to produce an as-built BIM model. Our algorithm, developed in C++, is based on plane extraction by means of the RANSAC algorithm followed by the minimization of the quadrate sum of points-plane distance. Moreover, this paper will take an in-depth look at the influence of data resolution in the accuracy of plane extraction and at the necessary accuracy for the construction of a BIM model. A laser scanner survey of a three floors building composed by 36 scan stations has produced a point cloud of about 550 million points. The estimated plane parameters at different data resolution are analysed in terms of distance from the full points cloud resolution

iDriving: Toward Safe and Efficient Infrastructure-directed Autonomous Driving

Autonomous driving will become pervasive in the coming decades. iDriving improves the safety of autonomous driving at intersections and increases efficiency by improving traffic throughput at intersections. In iDriving, roadside infrastructure remotely drives an autonomous vehicle at an intersection by offloading perception and planning from the vehicle to roadside infrastructure. To achieve this, iDriving must be able to process voluminous sensor data at full frame rate with a tail latency of less than 100 ms, without sacrificing accuracy. We describe algorithms and optimizations that enable it to achieve this goal using an accurate and lightweight perception component that reasons on composite views derived from overlapping sensors, and a planner that jointly plans trajectories for multiple vehicles. In our evaluations, iDriving always ensures safe passage of vehicles, while autonomous driving can only do so 27% of the time. iDriving also results in 5x lower wait times than other approaches because it enables traffic-light free intersections

Position estimation using a stereo camera as part of the perception system in a Formula Student car

This thesis presents a part of the implementation of the perception system in an autonomous Formula Student vehicle. More precisely, it develops two different pipelines to process the data from the two main sensors of the vehicle: a LiDAR and a stereo camera. The first, a stereo camera system which is based on two monocular cameras, provides traffic cone position estimations based on the detections made by a convolutional neural network. These positions are obtained by using a self-designed stereo processing algorithm, based on 2D-3D position estimates and keypoint extraction and matching. The second is a sensor fusion system that first registers both sensors based on an extrinsic calibration system that has been implemented. Then, it exploits the neural network detection from the stereo system to project the LiDAR point cloud onto the image, obtaining a balance between accurate detection and position estimation. These two systems are evaluated, compared and integrated into "Xaloc". The Formula Student vehicle developed by the Driverless UPC team.Esta tesis presenta una parte de la implementación del sistema de percepción en un vehículo autónomo de Formula Student. Concretamente, se desarrollan dos sistemas diferentes para el procesado de datos de los dos sensores principales del vehículo: un LiDAR y una cámara estéreo. El sistema de cámara estéreo se basa en dos cámaras monoculares y proporciona estimaciones de la posición de los conos de tráfico que delimitan la pista en base a las detecciones realizadas por una red neuronal convolucional. Estas posiciones se obtienen mediante el uso de un algoritmo de procesamiento estéreo de diseño propio, basado en estimaciones de posición 2D-3D y en extracción y correspondencia de "keypoints". El segundo es un sistema de fusión de sensores que primero registra ambos sensores basándose en un sistema de calibración extrínseco que se ha implementado. Luego, usa la detección hecha con la red neuronal del sistema estéreo para proyectar la nube de puntos LiDAR en la imagen, obteniendo un lo mejor de cada sensor: una detección robusta y una estimación de posición muy precisa. Estos dos sistemas se evalúan, comparan e integran en "Xaloc" el vehículo sin conductor del equipo de Formula Student Driverless UPC.Aquesta tesi presenta una part de la implementació del sistema de percepció en un vehicle autònom de Formula Student. En concret, es desenvolupen dos sistemes diferents per processar les dades dels dos principals sensors del vehicle: un LiDAR i una càmera estèreo. El sistema de càmera estèreo es basa en dues càmeres monoculars, i proporciona estimacions de les posicions dels cons de trànsit que delimiten la pista basades en les deteccions fetes amb una xarxa neuronal convolucional. Aquestes posicions s'obtenen mitjançant un algoritme de processament d'estèreo propi, basat en estimacions de posició 2D-3D i en extracció i correspondència de keypoints. El segon és un sistema de fusió de sensors que registra els dos sensors en base a un sistema de calibratge extrínsec que s'ha implementat. A continuació, fa servir les deteccions de la xarxa neuronal del sistema estèreo per projectar el núvol de punts LiDAR a la imatge, obtenint un equilibri entre una bona detecció en imatge i la precisió del núvol de punts LiDAR. Aquests dos sistemes són avaluats, comparats i integrats al "Xaloc" el vehicle sense conductor de l'equip de Formula Student Driverless UPC

er.autopilot 1.0: The Full Autonomous Stack for Oval Racing at High Speeds

The Indy Autonomous Challenge (IAC) brought together for the first time in history nine autonomous racing teams competing at unprecedented speed and in head-to-head scenario, using independently developed software on open-wheel racecars. This paper presents the complete software architecture used by team TII EuroRacing (TII-ER), covering all the modules needed to avoid static obstacles, perform active overtakes and reach speeds above 75 m/s (270 km/h). In addition to the most common modules related to perception, planning, and control, we discuss the approaches used for vehicle dynamics modelling, simulation, telemetry, and safety. Overall results and the performance of each module are described, as well as the lessons learned during the first two events of the competition on oval tracks, where the team placed respectively second and third.Comment: Preprint: Accepted to Field Robotics "Opportunities and Challenges with Autonomous Racing" Special Issu

Mapping and Real-Time Navigation With Application to Small UAS Urgent Landing

Small Unmanned Aircraft Systems (sUAS) operating in low-altitude airspace require flight near buildings and over people. Robust urgent landing capabilities including landing site selection are needed. However, conventional fixed-wing emergency landing sites such as open fields and empty roadways are rare in cities. This motivates our work to uniquely consider unoccupied flat rooftops as possible nearby landing sites. We propose novel methods to identify flat rooftop buildings, isolate their flat surfaces, and find touchdown points that maximize distance to obstacles. We model flat rooftop surfaces as polygons that capture their boundaries and possible obstructions on them. This thesis offers five specific contributions to support urgent rooftop landing. First, the Polylidar algorithm is developed which enables efficient non-convex polygon extraction with interior holes from 2D point sets. A key insight of this work is a novel boundary following method that contrasts computationally expensive geometric unions of triangles. Results from real-world and synthetic benchmarks show comparable accuracy and more than four times speedup compared to other state-of-the-art methods. Second, we extend polygon extraction from 2D to 3D data where polygons represent flat surfaces and interior holes representing obstacles. Our Polylidar3D algorithm transforms point clouds into a triangular mesh where dominant plane normals are identified and used to parallelize and regularize planar segmentation and polygon extraction. The result is a versatile and extremely fast algorithm for non-convex polygon extraction of 3D data. Third, we propose a framework for classifying roof shape (e.g., flat) within a city. We process satellite images, airborne LiDAR point clouds, and building outlines to generate both a satellite and depth image of each building. Convolutional neural networks are trained for each modality to extract high level features and sent to a random forest classifier for roof shape prediction. This research contributes the largest multi-city annotated dataset with over 4,500 rooftops used to train and test models. Our results show flat-like rooftops are identified with > 90% precision and recall. Fourth, we integrate Polylidar3D and our roof shape prediction model to extract flat rooftop surfaces from archived data sources. We uniquely identify optimal touchdown points for all landing sites. We model risk as an innovative combination of landing site and path risk metrics and conduct a multi-objective Pareto front analysis for sUAS urgent landing in cities. Our proposed emergency planning framework guarantees a risk-optimal landing site and flight plan is selected. Fifth, we verify a chosen rooftop landing site on real-time vertical approach with on-board LiDAR and camera sensors. Our method contributes an innovative fusion of semantic segmentation using neural networks with computational geometry that is robust to individual sensor and method failure. We construct a high-fidelity simulated city in the Unreal game engine with a statistically-accurate representation of rooftop obstacles. We show our method leads to greater than 4% improvement in accuracy for landing site identification compared to using LiDAR only. This work has broad impact for the safety of sUAS in cities as well as Urban Air Mobility (UAM). Our methods identify thousands of additional rooftop landing sites in cities which can provide safe landing zones in the event of emergencies. However, the maps we create are limited by the availability, accuracy, and resolution of archived data. Methods for quantifying data uncertainty or performing real-time map updates from a fleet of sUAS are left for future work.PHDRoboticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/170026/1/jdcasta_1.pd

Integrasjon av et minimalistisk sett av sensorer for kartlegging og lokalisering av landbruksroboter

Robots have recently become ubiquitous in many aspects of daily life. For in-house applications there is vacuuming, mopping and lawn-mowing robots. Swarms of robots have been used in Amazon warehouses for several years. Autonomous driving cars, despite being set back by several safety issues, are undeniably becoming the standard of the automobile industry. Not just being useful for commercial applications, robots can perform various tasks, such as inspecting hazardous sites, taking part in search-and-rescue missions. Regardless of end-user applications, autonomy plays a crucial role in modern robots. The essential capabilities required for autonomous operations are mapping, localization and navigation. The goal of this thesis is to develop a new approach to solve the problems of mapping, localization, and navigation for autonomous robots in agriculture. This type of environment poses some unique challenges such as repetitive patterns, large-scale sparse features environments, in comparison to other scenarios such as urban/cities, where the abundance of good features such as pavements, buildings, road lanes, traffic signs, etc., exists. In outdoor agricultural environments, a robot can rely on a Global Navigation Satellite System (GNSS) to determine its whereabouts. It is often limited to the robot's activities to accessible GNSS signal areas. It would fail for indoor environments. In this case, different types of exteroceptive sensors such as (RGB, Depth, Thermal) cameras, laser scanner, Light Detection and Ranging (LiDAR) and proprioceptive sensors such as Inertial Measurement Unit (IMU), wheel-encoders can be fused to better estimate the robot's states. Generic approaches of combining several different sensors often yield superior estimation results but they are not always optimal in terms of cost-effectiveness, high modularity, reusability, and interchangeability. For agricultural robots, it is equally important for being robust for long term operations as well as being cost-effective for mass production. We tackle this challenge by exploring and selectively using a handful of sensors such as RGB-D cameras, LiDAR and IMU for representative agricultural environments. The sensor fusion algorithms provide high precision and robustness for mapping and localization while at the same time assuring cost-effectiveness by employing only the necessary sensors for a task at hand. In this thesis, we extend the LiDAR mapping and localization methods for normal urban/city scenarios to cope with the agricultural environments where the presence of slopes, vegetation, trees render the traditional approaches to fail. Our mapping method substantially reduces the memory footprint for map storing, which is important for large-scale farms. We show how to handle the localization problem in dynamic growing strawberry polytunnels by using only a stereo visual-inertial (VI) and depth sensor to extract and track only invariant features. This eliminates the need for remapping to deal with dynamic scenes. Also, for a demonstration of the minimalistic requirement for autonomous agricultural robots, we show the ability to autonomously traverse between rows in a difficult environment of zigzag-liked polytunnel using only a laser scanner. Furthermore, we present an autonomous navigation capability by using only a camera without explicitly performing mapping or localization. Finally, our mapping and localization methods are generic and platform-agnostic, which can be applied to different types of agricultural robots. All contributions presented in this thesis have been tested and validated on real robots in real agricultural environments. All approaches have been published or submitted in peer-reviewed conference papers and journal articles.Roboter har nylig blitt standard i mange deler av hverdagen. I hjemmet har vi støvsuger-, vaske- og gressklippende roboter. Svermer med roboter har blitt brukt av Amazons varehus i mange år. Autonome selvkjørende biler, til tross for å ha vært satt tilbake av sikkerhetshensyn, er udiskutabelt på vei til å bli standarden innen bilbransjen. Roboter har mer nytte enn rent kommersielt bruk. Roboter kan utføre forskjellige oppgaver, som å inspisere farlige områder og delta i leteoppdrag. Uansett hva sluttbrukeren velger å gjøre, spiller autonomi en viktig rolle i moderne roboter. De essensielle egenskapene for autonome operasjoner i landbruket er kartlegging, lokalisering og navigering. Denne type miljø gir spesielle utfordringer som repetitive mønstre og storskala miljø med få landskapsdetaljer, sammenlignet med andre steder, som urbane-/bymiljø, hvor det finnes mange landskapsdetaljer som fortau, bygninger, trafikkfelt, trafikkskilt, etc. I utendørs jordbruksmiljø kan en robot bruke Global Navigation Satellite System (GNSS) til å navigere sine omgivelser. Dette begrenser robotens aktiviteter til områder med tilgjengelig GNSS signaler. Dette vil ikke fungere i miljøer innendørs. I ett slikt tilfelle vil reseptorer mot det eksterne miljø som (RGB-, dybde-, temperatur-) kameraer, laserskannere, «Light detection and Ranging» (LiDAR) og propriopsjonære detektorer som treghetssensorer (IMU) og hjulenkodere kunne brukes sammen for å bedre kunne estimere robotens tilstand. Generisk kombinering av forskjellige sensorer fører til overlegne estimeringsresultater, men er ofte suboptimale med hensyn på kostnadseffektivitet, moduleringingsgrad og utbyttbarhet. For landbruksroboter så er det like viktig med robusthet for lang tids bruk som kostnadseffektivitet for masseproduksjon. Vi taklet denne utfordringen med å utforske og selektivt velge en håndfull sensorer som RGB-D kameraer, LiDAR og IMU for representative landbruksmiljø. Algoritmen som kombinerer sensorsignalene gir en høy presisjonsgrad og robusthet for kartlegging og lokalisering, og gir samtidig kostnadseffektivitet med å bare bruke de nødvendige sensorene for oppgaven som skal utføres. I denne avhandlingen utvider vi en LiDAR kartlegging og lokaliseringsmetode normalt brukt i urbane/bymiljø til å takle landbruksmiljø, hvor hellinger, vegetasjon og trær gjør at tradisjonelle metoder mislykkes. Vår metode reduserer signifikant lagringsbehovet for kartlagring, noe som er viktig for storskala gårder. Vi viser hvordan lokaliseringsproblemet i dynamisk voksende jordbær-polytuneller kan løses ved å bruke en stereo visuel inertiel (VI) og en dybdesensor for å ekstrahere statiske objekter. Dette eliminerer behovet å kartlegge på nytt for å klare dynamiske scener. I tillegg demonstrerer vi de minimalistiske kravene for autonome jordbruksroboter. Vi viser robotens evne til å bevege seg autonomt mellom rader i ett vanskelig miljø med polytuneller i sikksakk-mønstre ved bruk av kun en laserskanner. Videre presenterer vi en autonom navigeringsevne ved bruk av kun ett kamera uten å eksplisitt kartlegge eller lokalisere. Til slutt viser vi at kartleggings- og lokaliseringsmetodene er generiske og platform-agnostiske, noe som kan brukes med flere typer jordbruksroboter. Alle bidrag presentert i denne avhandlingen har blitt testet og validert med ekte roboter i ekte landbruksmiljø. Alle forsøk har blitt publisert eller sendt til fagfellevurderte konferansepapirer og journalartikler