Physics-based Simulation of Continuous-Wave LIDAR for Localization, Calibration and Tracking

Light Detection and Ranging (LIDAR) sensors play an important role in the perception stack of autonomous robots, supplying mapping and localization pipelines with depth measurements of the environment. While their accuracy outperforms other types of depth sensors, such as stereo or time-of-flight cameras, the accurate modeling of LIDAR sensors requires laborious manual calibration that typically does not take into account the interaction of laser light with different surface types, incidence angles and other phenomena that significantly influence measurements. In this work, we introduce a physically plausible model of a 2D continuous-wave LIDAR that accounts for the surface-light interactions and simulates the measurement process in the Hokuyo URG-04LX LIDAR. Through automatic differentiation, we employ gradient-based optimization to estimate model parameters from real sensor measurements.Comment: Published at ICRA 202

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

Active Metric-Semantic Mapping by Multiple Aerial Robots

Traditional approaches for active mapping focus on building geometric maps. For most real-world applications, however, actionable information is related to semantically meaningful objects in the environment. We propose an approach to the active metric-semantic mapping problem that enables multiple heterogeneous robots to collaboratively build a map of the environment. The robots actively explore to minimize the uncertainties in both semantic (object classification) and geometric (object modeling) information. We represent the environment using informative but sparse object models, each consisting of a basic shape and a semantic class label, and characterize uncertainties empirically using a large amount of real-world data. Given a prior map, we use this model to select actions for each robot to minimize uncertainties. The performance of our algorithm is demonstrated through multi-robot experiments in diverse real-world environments. The proposed framework is applicable to a wide range of real-world problems, such as precision agriculture, infrastructure inspection, and asset mapping in factories. A demo video can be found at https://youtu.be/S86SgXi54oU.Comment: ICRA 2023 (2023 International Conference on Robotics and Automation

Robust 3D IMU-LIDAR Calibration and Multi Sensor Probabilistic State Estimation

Autonomous robots are highly complex systems. In order to operate in dynamic environments, adaptability in their decision-making algorithms is a must. Thus, the internal and external information that robots obtain from sensors is critical to re-evaluate their decisions in real time. Accuracy is key in this endeavor, both from the hardware side and the modeling point of view. In order to guarantee the highest performance, sensors need to be correctly calibrated. To this end, some parameters are tuned so that the particular realization of a sensor best matches a generalized mathematical model. This step grows in complexity with the integration of multiple sensors, which is generally a requirement in order to cope with the dynamic nature of real world applications. This project aims to deal with the calibration of an inertial measurement unit, or IMU, and a Light Detection and Ranging device, or LiDAR. An offline batch optimization procedure is proposed to optimally estimate the intrinsic and extrinsic parameters of the model. Then, an online state estimation module that makes use of the aforementioned parameters and the fusion of LiDAR-inertial data for local navigation is proposed. Additionally, it incorporates real time corrections to account for the time-varying nature of the model, essential to deal with exposure to continued operation and wear and tear. Keywords: sensor fusion, multi-sensor calibration, factor graphs, batch optimization, Gaussian Processes, state estimation, LiDAR-inertial odometry, Error State Kalman Filter, Normal Distributions Transform

Collaborative autonomy in heterogeneous multi-robot systems

As autonomous mobile robots become increasingly connected and widely deployed in different domains, managing multiple robots and their interaction is key to the future of ubiquitous autonomous systems. Indeed, robots are not individual entities anymore. Instead, many robots today are deployed as part of larger fleets or in teams. The benefits of multirobot collaboration, specially in heterogeneous groups, are multiple. Significantly higher degrees of situational awareness and understanding of their environment can be achieved when robots with different operational capabilities are deployed together. Examples of this include the Perseverance rover and the Ingenuity helicopter that NASA has deployed in Mars, or the highly heterogeneous robot teams that explored caves and other complex environments during the last DARPA Sub-T competition. This thesis delves into the wide topic of collaborative autonomy in multi-robot systems, encompassing some of the key elements required for achieving robust collaboration: solving collaborative decision-making problems; securing their operation, management and interaction; providing means for autonomous coordination in space and accurate global or relative state estimation; and achieving collaborative situational awareness through distributed perception and cooperative planning. The thesis covers novel formation control algorithms, and new ways to achieve accurate absolute or relative localization within multi-robot systems. It also explores the potential of distributed ledger technologies as an underlying framework to achieve collaborative decision-making in distributed robotic systems. Throughout the thesis, I introduce novel approaches to utilizing cryptographic elements and blockchain technology for securing the operation of autonomous robots, showing that sensor data and mission instructions can be validated in an end-to-end manner. I then shift the focus to localization and coordination, studying ultra-wideband (UWB) radios and their potential. I show how UWB-based ranging and localization can enable aerial robots to operate in GNSS-denied environments, with a study of the constraints and limitations. I also study the potential of UWB-based relative localization between aerial and ground robots for more accurate positioning in areas where GNSS signals degrade. In terms of coordination, I introduce two new algorithms for formation control that require zero to minimal communication, if enough degree of awareness of neighbor robots is available. These algorithms are validated in simulation and real-world experiments. The thesis concludes with the integration of a new approach to cooperative path planning algorithms and UWB-based relative localization for dense scene reconstruction using lidar and vision sensors in ground and aerial robots

Localization Algorithms for GNSS-denied and Challenging Environments

In this dissertation, the problem about localization in GNSS-denied and challenging environments is addressed. Specifically, the challenging environments discussed in this dissertation include two different types, environments including only low-resolution features and environments containing moving objects. To achieve accurate pose estimates, the errors are always bounded through matching observations from sensors with surrounding environments. These challenging environments, unfortunately, would bring troubles into matching related methods, such as fingerprint matching, and ICP. For instance, in environments with low-resolution features, the on-board sensor measurements could match to multiple positions on a map, which creates ambiguity; in environments with moving objects included, the accuracy of the estimated localization is affected by the moving objects when performing matching. In this dissertation, two sensor fusion based strategies are proposed to solve localization problems with respect to these two types of challenging environments, respectively. For environments with only low-resolution features, such as flying over sea or desert, a multi-agent localization algorithm using pairwise communication with ranging and magnetic anomaly measurements is proposed in this dissertation. A scalable framework is then presented to extend the multi-agent localization algorithm to be suitable for a large group of agents (e.g., 128 agents) through applying CI algorithm. The simulation results show that the proposed algorithm is able to deal with large group sizes, achieve 10 meters level localization performance with 180 km traveling distance, while under restrictive communication constraints. For environments including moving objects, lidar-inertial-based solutions are proposed and tested in this dissertation. Inspired by the CI algorithm presented above, a potential solution using multiple features motions estimate and tracking is analyzed. In order to improve the performance and effectiveness of the potential solution, a lidar-inertial based SLAM algorithm is then proposed. In this method, an efficient tightly-coupled iterated Kalman filter with a build-in dynamic object filter is designed as the front-end of the SLAM algorithm, and the factor graph strategy using a scan context technology as the loop closure detection is utilized as the back-end. The performance of the proposed lidar-inertial based SLAM algorithm is evaluated with several data sets collected in environments including moving objects, and compared with the state-of-the-art lidar-inertial based SLAM algorithms

A Robust Localization Solution for an Uncrewed Ground Vehicle in Unstructured Outdoor GNSS-Denied Environments

This work addresses the challenge of developing a localization system for an uncrewed ground vehicle (UGV) operating autonomously in unstructured outdoor Global Navigation Satellite System (GNSS)-denied environments. The goal is to enable accurate mapping and long-range navigation with practical applications in domains such as autonomous construction, military engineering missions, and exploration of non-Earth planets. The proposed system - Terrain-Referenced Assured Engineer Localization System (TRAELS) - integrates pose estimates produced by two complementary terrain referenced navigation (TRN) methods with wheel odometry and inertial measurement unit (IMU) measurements using an Extended Kalman Filter (EKF). Unlike simultaneous localization and mapping (SLAM) systems that require loop closures, the described approach maintains accuracy over long distances and one-way missions without the need to revisit previous positions. Evaluation of TRAELS is performed across a range of environments. In regions where a combination of distinctive geometric and ground surface features are present, the developed TRN methods are leveraged by TRAELS to consistently achieve an absolute trajectory error of less than 3.0 m. The approach is also shown to be capable of recovering from large accumulated drift when traversing feature-sparse areas, which is essential in ensuring robust performance of the system across a wide variety of challenging GNSS-denied environments. Overall, the effectiveness of the system in providing precise localization and mapping capabilities in challenging GNSS-denied environments is demonstrated and an analysis is performed leading to insights for improving TRN approaches for UGVs.Comment: 15 pages, 9 figures, 2 tables, to be published in The Proceedings of the Institute of Navigation GNSS+ 2023 conference (ION GNSS+ 23