An Effective Multi-Cue Positioning System for Agricultural Robotics

The self-localization capability is a crucial component for Unmanned Ground Vehicles (UGV) in farming applications. Approaches based solely on visual cues or on low-cost GPS are easily prone to fail in such scenarios. In this paper, we present a robust and accurate 3D global pose estimation framework, designed to take full advantage of heterogeneous sensory data. By modeling the pose estimation problem as a pose graph optimization, our approach simultaneously mitigates the cumulative drift introduced by motion estimation systems (wheel odometry, visual odometry, ...), and the noise introduced by raw GPS readings. Along with a suitable motion model, our system also integrates two additional types of constraints: (i) a Digital Elevation Model and (ii) a Markov Random Field assumption. We demonstrate how using these additional cues substantially reduces the error along the altitude axis and, moreover, how this benefit spreads to the other components of the state. We report exhaustive experiments combining several sensor setups, showing accuracy improvements ranging from 37% to 76% with respect to the exclusive use of a GPS sensor. We show that our approach provides accurate results even if the GPS unexpectedly changes positioning mode. The code of our system along with the acquired datasets are released with this paper.Comment: Accepted for publication in IEEE Robotics and Automation Letters, 201

Cooperative UAV–UGV autonomous power pylon inspection: an investigation of cooperative outdoor vehicle positioning architecture

Realizing autonomous inspection, such as that of power distribution lines, through unmanned aerial vehicle (UAV) systems is a key research domain in robotics. In particular, the use of autonomous and semi-autonomous vehicles to execute the tasks of an inspection process can enhance the efficacy and safety of the operation; however, many technical problems, such as those pertaining to the precise positioning and path following of the vehicles, robust obstacle detection, and intelligent control, must be addressed. In this study, an innovative architecture involving an unmanned aircraft vehicle (UAV) and an unmanned ground vehicle (UGV) was examined for detailed inspections of power lines. In the proposed strategy, each vehicle provides its position information to the other, which ensures a safe inspection process. The results of real-world experiments indicate a satisfactory performance, thereby demonstrating the feasibility of the proposed approach.This research was funded by National Counsel of Technological and Scientific Development of Brazil (CNPq). The authors thank the National Counsel of Technological and Scientific Development of Brazil (CNPq); Coordination for the Improvement of Higher Level People (CAPES); and the Brazilian Ministry of Science, Technology, Innovation, and Communication (MCTIC). The authors would also like express their deepest gratitude to Control Robotics for sharing the Pioneer P3 robot for the experiments. Thanks to Leticia Cantieri for editing the experiment video.info:eu-repo/semantics/publishedVersio

Contrastive Label Disambiguation for Self-Supervised Terrain Traversability Learning in Off-Road Environments

Discriminating the traversability of terrains is a crucial task for autonomous driving in off-road environments. However, it is challenging due to the diverse, ambiguous, and platform-specific nature of off-road traversability. In this paper, we propose a novel self-supervised terrain traversability learning framework, utilizing a contrastive label disambiguation mechanism. Firstly, weakly labeled training samples with pseudo labels are automatically generated by projecting actual driving experiences onto the terrain models constructed in real time. Subsequently, a prototype-based contrastive representation learning method is designed to learn distinguishable embeddings, facilitating the self-supervised updating of those pseudo labels. As the iterative interaction between representation learning and pseudo label updating, the ambiguities in those pseudo labels are gradually eliminated, enabling the learning of platform-specific and task-specific traversability without any human-provided annotations. Experimental results on the RELLIS-3D dataset and our Gobi Desert driving dataset demonstrate the effectiveness of the proposed method.Comment: 9 pages, 11 figure

Influence of complex environments on LiDAR-Based robot navigation

La navigation sécuritaire et efficace des robots mobiles repose grandement sur l’utilisation des capteurs embarqués. L’un des capteurs qui est de plus en plus utilisé pour cette tâche est le Light Detection And Ranging (LiDAR). Bien que les recherches récentes montrent une amélioration des performances de navigation basée sur les LiDARs, faire face à des environnements non structurés complexes ou des conditions météorologiques difficiles reste problématique. Dans ce mémoire, nous présentons une analyse de l’influence de telles conditions sur la navigation basée sur les LiDARs. Notre première contribution est d’évaluer comment les LiDARs sont affectés par les flocons de neige durant les tempêtes de neige. Pour ce faire, nous créons un nouvel ensemble de données en faisant l’acquisition de données durant six précipitations de neige. Une analyse statistique de ces ensembles de données, nous caractérisons la sensibilité de chaque capteur et montrons que les mesures de capteurs peuvent être modélisées de manière probabilistique. Nous montrons aussi que les précipitations de neige ont peu d’influence au-delà de 10 m. Notre seconde contribution est d’évaluer l’impact de structures tridimensionnelles complexes présentes en forêt sur les performances d’un algorithme de reconnaissance d’endroits. Nous avons acquis des données dans un environnement extérieur structuré et en forêt, ce qui permet d’évaluer l’influence de ces derniers sur les performances de reconnaissance d’endroits. Notre hypothèse est que, plus deux balayages laser sont proches l’un de l’autre, plus la croyance que ceux-ci proviennent du même endroit sera élevée, mais modulé par le niveau de complexité de l’environnement. Nos expériences confirment que la forêt, avec ses réseaux de branches compliqués et son feuillage, produit plus de données aberrantes et induit une chute plus rapide des performances de reconnaissance en fonction de la distance. Notre conclusion finale est que, les environnements complexes étudiés influencent négativement les performances de navigation basée sur les LiDARs, ce qui devrait être considéré pour développer des algorithmes de navigation robustes.To ensure safe and efficient navigation, mobile robots heavily rely on their ability to use on-board sensors. One such sensor, increasingly used for robot navigation, is the Light Detection And Ranging (LiDAR). Although recent research showed improvement in LiDAR-based navigation, dealing with complex unstructured environments or difficult weather conditions remains problematic. In this thesis, we present an analysis of the influence of such challenging conditions on LiDAR-based navigation. Our first contribution is to evaluate how LiDARs are affected by snowflakes during snowstorms. To this end, we create a novel dataset by acquiring data during six snowfalls using four sensors simultaneously. Based on statistical analysis of this dataset, we characterized the sensitivity of each device and showed that sensor measurements can be modelled in a probabilistic manner. We also showed that falling snow has little impact beyond a range of 10 m. Our second contribution is to evaluate the impact of complex of three-dimensional structures, present in forests, on the performance of a LiDAR-based place recognition algorithm. We acquired data in structured outdoor environment and in forest, which allowed evaluating the impact of the environment on the place recognition performance. Our hypothesis was that the closer two scans are acquired from each other, the higher the belief that the scans originate from the same place will be, but modulated by the level of complexity of the environments. Our experiments confirmed that forests, with their intricate network of branches and foliage, produce more outliers and induce recognition performance to decrease more quickly with distance when compared with structured outdoor environment. Our conclusion is that falling snow conditions and forest environments negatively impact LiDAR-based navigation performance, which should be considered to develop robust navigation algorithms

Systems engineering approach to develop guidance, navigation and control algorithms for unmanned ground vehicle

Despite the growing popularity of unmanned systems being deployed in the military domain, limited research efforts have been dedicated to the progress of ground system developments. Dedicated efforts for unmanned ground vehicles (UGV) focused largely on operations in continental environments, places where vegetation is relatively sparse compared to a tropical jungle or plantation estate commonly found in Asia. This research explore methods for the development of an UGV that would be capable of operating autonomously in a densely cluttered environment such as that found in Asia. This thesis adopted a systems engineering approach to understand the pertinent parameters affecting the performance of the UGV in order to evaluate, design and develop the necessary guidance, navigation and control algorithms for the UGV. The thesis uses methodologies such as the pure pursuit method for path following and the vector field histogram method for obstacle avoidance as the main guidance and control algorithm governing the movement of the UGV. The thesis then considers the use of feature recognition method of image processing to form the basis of the target identification and tracking algorithm.http://archive.org/details/systemsengineeri1094550579Outstanding ThesisMajor, Republic of Singapore ArmyApproved for public release; distribution is unlimited

Four years of multi-modal odometry and mapping on the rail vehicles

Precise, seamless, and efficient train localization as well as long-term railway environment monitoring is the essential property towards reliability, availability, maintainability, and safety (RAMS) engineering for railroad systems. Simultaneous localization and mapping (SLAM) is right at the core of solving the two problems concurrently. In this end, we propose a high-performance and versatile multi-modal framework in this paper, targeted for the odometry and mapping task for various rail vehicles. Our system is built atop an inertial-centric state estimator that tightly couples light detection and ranging (LiDAR), visual, optionally satellite navigation and map-based localization information with the convenience and extendibility of loosely coupled methods. The inertial sensors IMU and wheel encoder are treated as the primary sensor, which achieves the observations from subsystems to constrain the accelerometer and gyroscope biases. Compared to point-only LiDAR-inertial methods, our approach leverages more geometry information by introducing both track plane and electric power pillars into state estimation. The Visual-inertial subsystem also utilizes the environmental structure information by employing both lines and points. Besides, the method is capable of handling sensor failures by automatic reconfiguration bypassing failure modules. Our proposed method has been extensively tested in the long-during railway environments over four years, including general-speed, high-speed and metro, both passenger and freight traffic are investigated. Further, we aim to share, in an open way, the experience, problems, and successes of our group with the robotics community so that those that work in such environments can avoid these errors. In this view, we open source some of the datasets to benefit the research community

VEHÍCULOS TERRESTRES NO TRIPULADOS, SUS APLICACIONES Y TECNOLOGÍAS DE IMPLEMENTACIÓN

Unmanned ground vehicles are considered semi-autonomous or autonomous machines that perform complex operations of transport and monitoring of physical and environmental variables; to mention a few. These vehicles allow for the customization, optimization, and flexibility of the demands and challenges of innovation in multiple industry applications such as mapping, agriculture, security, mining, telemetry, military, geoscience, environmental, and logistics; therefore, we believe that consolidating the scientific information published around this topic allows readers to understand the connections between different approaches, applications, and enabling technologies to determine the direction in which they wish to take their research; and, at the same time, promotes more discussion about the fusion of mobile robotics into the internet applications of things that are emerging in today's industry. In this article, the web tool "Tree of Science" and the Systematic Review for information analysis were implemented.Los vehículos terrestres no tripulados son considerados máquinas semi autónomas o autónomas que realizan operaciones complejas de transporte y monitoreo de variables físicas y ambientales; por mencionar algunas. Estos vehículos permiten personalizar, optimizar y dar flexibilidad a las demandas y desafíos de innovación en múltiples campos de aplicación en la industria como cartografía, agricultura, seguridad, minería, telemetría, militar, geociencia, ambiental y logística; por tanto, creemos que consolidar la información científica publicada alrededor de este tema permite a los lectores comprender las conexiones entre los diferentes enfoques, aplicaciones y tecnologías habilitadoras para determinar el rumbo al cual desean llevar su investigación; y, al mismo tiempo, promover más debates sobre la fusión de la robótica móvil en las aplicaciones de internet de las cosas que están emergiendo en la industrial actual. En este artículo se implementó la herramienta web “Tree of Science” y la Revisión Sistemática para el análisis de la información

Implementation 2D EKF-Based Simultaneous Localisation and Mapping for Mobile Robot

The main goal of this project is that the basic EKF-based SLAM operation can be implemented sufficiently for estimating the state of the UGV that is operated in this real environment involving dynamic objects. Several problems in practical implementation of SLAM operation such as processing measurement data, removing bias measurement, extracting landmarks from the measurement data, pre-filtering extracted landmarks and data association in the observed landmarks are observed during the operation of EKF-based SLAM system . In addition, the comparison of EKF-based SLAM operation with dead reckoning operation and Global Positioning System (GPS) are also performed to determine the effectiveness and performance of EKF-based SLAM operation in the real environment

Change of Scenery: Unsupervised LiDAR Change Detection for Mobile Robots

This paper presents a fully unsupervised deep change detection approach for mobile robots with 3D LiDAR. In unstructured environments, it is infeasible to define a closed set of semantic classes. Instead, semantic segmentation is reformulated as binary change detection. We develop a neural network, RangeNetCD, that uses an existing point-cloud map and a live LiDAR scan to detect scene changes with respect to the map. Using a novel loss function, existing point-cloud semantic segmentation networks can be trained to perform change detection without any labels or assumptions about local semantics. We demonstrate the performance of this approach on data from challenging terrains; mean intersection over union (mIoU) scores range between 67.4% and 82.2% depending on the amount of environmental structure. This outperforms the geometric baseline used in all experiments. The neural network runs faster than 10Hz and is integrated into a robot's autonomy stack to allow safe navigation around obstacles that intersect the planned path. In addition, a novel method for the rapid automated acquisition of per-point ground-truth labels is described. Covering changed parts of the scene with retroreflective materials and applying a threshold filter to the intensity channel of the LiDAR allows for quantitative evaluation of the change detector.Comment: 7 pages (6 content, 1 references). 7 figures, submitted to the 2024 IEEE International Conference on Robotics and Automation (ICRA