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

Low-Resolution Vision for Autonomous Mobile Robots

The goal of this research is to develop algorithms using low-resolution images to perceive and understand a typical indoor environment and thereby enable a mobile robot to autonomously navigate such an environment. We present techniques for three problems: autonomous exploration, corridor classification, and minimalistic geometric representation of an indoor environment for navigation. First, we present a technique for mobile robot exploration in unknown indoor environments using only a single forward-facing camera. Rather than processing all the data, the method intermittently examines only small 32X24 downsampled grayscale images. We show that for the task of indoor exploration the visual information is highly redundant, allowing successful navigation even using only a small fraction (0.02%) of the available data. The method keeps the robot centered in the corridor by estimating two state parameters: the orientation within the corridor and the distance to the end of the corridor. The orientation is determined by combining the results of five complementary measures, while the estimated distance to the end combines the results of three complementary measures. These measures, which are predominantly information-theoretic, are analyzed independently, and the combined system is tested in several unknown corridor buildings exhibiting a wide variety of appearances, showing the sufficiency of low-resolution visual information for mobile robot exploration. Because the algorithm discards such a large percentage (99.98%) of the information both spatially and temporally, processing occurs at an average of 1000 frames per second, or equivalently takes a small fraction of the CPU. Second, we present an algorithm using image entropy to detect and classify corridor junctions from low resolution images. Because entropy can be used to perceive depth, it can be used to detect an open corridor in a set of images recorded by turning a robot at a junction by 360 degrees. Our algorithm involves detecting peaks from continuously measured entropy values and determining the angular distance between the detected peaks to determine the type of junction that was recorded (either middle, L-junction, T-junction, dead-end, or cross junction). We show that the same algorithm can be used to detect open corridors from both monocular as well as omnidirectional images. Third, we propose a minimalistic corridor representation consisting of the orientation line (center) and the wall-floor boundaries (lateral limit). The representation is extracted from low-resolution images using a novel combination of information theoretic measures and gradient cues. Our study investigates the impact of image resolution upon the accuracy of extracting such a geometry, showing that centerline and wall-floor boundaries can be estimated with reasonable accuracy even in texture-poor environments with low-resolution images. In a database of 7 unique corridor sequences for orientation measurements, less than 2% additional error was observed as the resolution of the image decreased by 99.9%

Pose Estimation With Low-Resolution Bearing-Only Sensors

Pose estimation of neighboring robots is a key requirement for configuration control behaviors in multi-robot systems. Estimating pose is difficult without system constraints, it is even more challenging when using minimalistic sensing alongside limited bandwidth. Minimal sensing models are a well studied field in robotics and are relevant to our particular hardware platform, the r-one, which has sensors that only measure a low-resolution bearing to neighboring robots. These bearing-only sensors are simpler to design with and cheaper to deploy in large numbers. In this thesis, I focus on the r-one multi-robot system which is capable of coarsely measuring the bearing, but not the distance, to neighbors. These sensors have a angular resolution of only 22.5 degrees due to the construction of the infrared system. I develop a particle filter algorithm that allows the r-one robot to estimate the pose of a neighbor using the infrared communication system and odometry measurements. This algorithm relies on the fusion of a coarse bearing measurement and neighbor velocities and is optimized to use the smallest communications bandwidth possible. I tested this algorithm with a simulation to demonstrate its effectiveness across varying sensor setups, neighbor update periods, and number of particles

Study and development of a reliable fiducials-based localization system for multicopter UAVs flying indoor

openThe recent evolution of technology in automation, agriculture, IoT, and aerospace fields has created a growing demand for mobile robots capable of autonomous operation and movement to accomplish various tasks. Aerial platforms are expected to play a central role in the future due to their versatility and swift intervention capabilities. However, the effective utilization of these platforms faces a significant challenge due to localization, which is a vital aspect for their interaction with the surrounding environment. While GNSS localization systems have established themselves as reliable solutions for open-space scenarios, the same approach is not viable for indoor settings, where localization remains an open problem as it is witnessed by the lack of extensive literature on the topic. In this thesis, we address this challenge by proposing a dependable solution for small multi-rotor UAVs using a Visual Inertial Odometry localization system. Our KF-based localization system reconstructs the pose by fusing data from onboard sensors. The primary source of information stems from the recognition of AprilTags fiducial markers, strategically placed in known positions to form a “map”. Building upon prior research and thesis work conducted at our university, we extend and enhance this system. We begin with a concise introduction, followed by a justification of our chosen strategies based on the current state of the art. We provide an overview of the key theoretical, mathematical, and technical aspects that support our work. These concepts are fundamental to the design of innovative strategies that address challenges such as data fusion from different AprilTag recognition and the elimination of misleading measurements. To validate our algorithms and their implementation, we conduct experimental tests using two distinct platforms by using localization accuracy and computational complexity as performance indices to demonstrate the practical viability of our proposed system. By tackling the critical issue of indoor localization for aerial platforms, this thesis tries to give some contribution to the advancement of robotics technology, opening avenues for enhanced autonomy and efficiency across various domains.The recent evolution of technology in automation, agriculture, IoT, and aerospace fields has created a growing demand for mobile robots capable of autonomous operation and movement to accomplish various tasks. Aerial platforms are expected to play a central role in the future due to their versatility and swift intervention capabilities. However, the effective utilization of these platforms faces a significant challenge due to localization, which is a vital aspect for their interaction with the surrounding environment. While GNSS localization systems have established themselves as reliable solutions for open-space scenarios, the same approach is not viable for indoor settings, where localization remains an open problem as it is witnessed by the lack of extensive literature on the topic. In this thesis, we address this challenge by proposing a dependable solution for small multi-rotor UAVs using a Visual Inertial Odometry localization system. Our KF-based localization system reconstructs the pose by fusing data from onboard sensors. The primary source of information stems from the recognition of AprilTags fiducial markers, strategically placed in known positions to form a “map”. Building upon prior research and thesis work conducted at our university, we extend and enhance this system. We begin with a concise introduction, followed by a justification of our chosen strategies based on the current state of the art. We provide an overview of the key theoretical, mathematical, and technical aspects that support our work. These concepts are fundamental to the design of innovative strategies that address challenges such as data fusion from different AprilTag recognition and the elimination of misleading measurements. To validate our algorithms and their implementation, we conduct experimental tests using two distinct platforms by using localization accuracy and computational complexity as performance indices to demonstrate the practical viability of our proposed system. By tackling the critical issue of indoor localization for aerial platforms, this thesis tries to give some contribution to the advancement of robotics technology, opening avenues for enhanced autonomy and efficiency across various domains

Visual slam in dynamic environments

El problema de localización y construcción visual simultánea de mapas (visual SLAM por sus siglas en inglés Simultaneous Localization and Mapping) consiste en localizar una cámara en un mapa que se construye de manera online. Esta tecnología permite la localización de robots en entornos desconocidos y la creación de un mapa de la zona con los sensores que lleva incorporados, es decir, sin contar con ninguna infraestructura externa. A diferencia de los enfoques de odometría en los cuales el movimiento incremental es integrado en el tiempo, un mapa permite que el sensor se localice continuamente en el mismo entorno sin acumular deriva.Asumir que la escena observada es estática es común en los algoritmos de SLAM visual. Aunque la suposición estática es válida para algunas aplicaciones, limita su utilidad en escenas concurridas del mundo real para la conducción autónoma, los robots de servicio o realidad aumentada y virtual entre otros. La detección y el estudio de objetos dinámicos es un requisito para estimar con precisión la posición del sensor y construir mapas estables, útiles para aplicaciones robóticas que operan a largo plazo.Las contribuciones principales de esta tesis son tres: 1. Somos capaces de detectar objetos dinámicos con la ayuda del uso de la segmentación semántica proveniente del aprendizaje profundo y el uso de enfoques de geometría multivisión. Esto nos permite lograr una precisión en la estimación de la trayectoria de la cámara en escenas altamente dinámicas comparable a la que se logra en entornos estáticos, así como construir mapas en 3D que contienen sólo la estructura del entorno estático y estable. 2. Logramos alucinar con imágenes realistas la estructura estática de la escena detrás de los objetos dinámicos. Esto nos permite ofrecer mapas completos con una representación plausible de la escena sin discontinuidades o vacíos ocasionados por las oclusiones de los objetos dinámicos. El reconocimiento visual de lugares también se ve impulsado por estos avances en el procesamiento de imágenes. 3. Desarrollamos un marco conjunto tanto para resolver el problema de SLAM como el seguimiento de múltiples objetos con el fin de obtener un mapa espacio-temporal con información de la trayectoria del sensor y de los alrededores. La comprensión de los objetos dinámicos circundantes es de crucial importancia para los nuevos requisitos de las aplicaciones emergentes de realidad aumentada/virtual o de la navegación autónoma. Estas tres contribuciones hacen avanzar el estado del arte en SLAM visual. Como un producto secundario de nuestra investigación y para el beneficio de la comunidad científica, hemos liberado el código que implementa las soluciones propuestas.<br /

Camera localization using trajectories and maps

We propose a new Bayesian framework for automatically determining the position (location and orientation) of an uncalibrated camera using the observations of moving objects and a schematic map of the passable areas of the environment. Our approach takes advantage of static and dynamic information on the scene structures through prior probability distributions for object dynamics. The proposed approach restricts plausible positions where the sensor can be located while taking into account the inherent ambiguity of the given setting. The proposed framework samples from the posterior probability distribution for the camera position via data driven MCMC, guided by an initial geometric analysis that restricts the search space. A Kullback-Leibler divergence analysis is then used that yields the final camera position estimate, while explicitly isolating ambiguous settings. The proposed approach is evaluated in synthetic and real environments, showing its satisfactory performance in both ambiguous and unambiguous settings

Learning cognitive maps: Finding useful structure in an uncertain world

In this chapter we will describe the central mechanisms that influence how people learn about large-scale space. We will focus particularly on how these mechanisms enable people to effectively cope with both the uncertainty inherent in a constantly changing world and also with the high information content of natural environments. The major lessons are that humans get by with a less is more approach to building structure, and that they are able to quickly adapt to environmental changes thanks to a range of general purpose mechanisms. By looking at abstract principles, instead of concrete implementation details, it is shown that the study of human learning can provide valuable lessons for robotics. Finally, these issues are discussed in the context of an implementation on a mobile robot. © 2007 Springer-Verlag Berlin Heidelberg