Reconstructing partially visible models using stereo vision, structured light, and the g2o framework

ABSTRACT This paper describes a framework for model-based 3D reconstruction of vines and trellising for a robot equipped with stereo cameras and structured light. In each frame, high-level 2D features, and a sparse set of 3D structured light points are found. Detected features are matched to 3D model components, and the g2o optimisation framework is used to estimate both the model's structure and the camera's trajectory. The system is demonstrated reconstructing the trellising present in images of vines, together with the camera's trajectory, over a 12m track consisting of 360 sets of frames. The estimated model is structurally correct and is almost complete, and the estimated trajectory drifts by just 4%. Future work will extend the framework to reconstruct the more complex structure of the vines

Robots for Exploration, Digital Preservation and Visualization of Archeological Sites

Monitoring and conservation of archaeological sites are important activities necessary to prevent damage or to perform restoration on cultural heritage. Standard techniques, like mapping and digitizing, are typically used to document the status of such sites. While these task are normally accomplished manually by humans, this is not possible when dealing with hard-to-access areas. For example, due to the possibility of structural collapses, underground tunnels like catacombs are considered highly unstable environments. Moreover, they are full of radioactive gas radon that limits the presence of people only for few minutes. The progress recently made in the artificial intelligence and robotics field opened new possibilities for mobile robots to be used in locations where humans are not allowed to enter. The ROVINA project aims at developing autonomous mobile robots to make faster, cheaper and safer the monitoring of archaeological sites. ROVINA will be evaluated on the catacombs of Priscilla (in Rome) and S. Gennaro (in Naples)

Structureless Camera Motion Estimation of Unordered Omnidirectional Images

This work aims at providing a novel camera motion estimation pipeline from large collections of unordered omnidirectional images. In oder to keep the pipeline as general and flexible as possible, cameras are modelled as unit spheres, allowing to incorporate any central camera type. For each camera an unprojection lookup is generated from intrinsics, which is called P2S-map (Pixel-to-Sphere-map), mapping pixels to their corresponding positions on the unit sphere. Consequently the camera geometry becomes independent of the underlying projection model. The pipeline also generates P2S-maps from world map projections with less distortion effects as they are known from cartography. Using P2S-maps from camera calibration and world map projection allows to convert omnidirectional camera images to an appropriate world map projection in oder to apply standard feature extraction and matching algorithms for data association. The proposed estimation pipeline combines the flexibility of SfM (Structure from Motion) - which handles unordered image collections - with the efficiency of PGO (Pose Graph Optimization), which is used as back-end in graph-based Visual SLAM (Simultaneous Localization and Mapping) approaches to optimize camera poses from large image sequences. SfM uses BA (Bundle Adjustment) to jointly optimize camera poses (motion) and 3d feature locations (structure), which becomes computationally expensive for large-scale scenarios. On the contrary PGO solves for camera poses (motion) from measured transformations between cameras, maintaining optimization managable. The proposed estimation algorithm combines both worlds. It obtains up-to-scale transformations between image pairs using two-view constraints, which are jointly scaled using trifocal constraints. A pose graph is generated from scaled two-view transformations and solved by PGO to obtain camera motion efficiently even for large image collections. Obtained results can be used as input data to provide initial pose estimates for further 3d reconstruction purposes e.g. to build a sparse structure from feature correspondences in an SfM or SLAM framework with further refinement via BA. The pipeline also incorporates fixed extrinsic constraints from multi-camera setups as well as depth information provided by RGBD sensors. The entire camera motion estimation pipeline does not need to generate a sparse 3d structure of the captured environment and thus is called SCME (Structureless Camera Motion Estimation).:1 Introduction 1.1 Motivation 1.1.1 Increasing Interest of Image-Based 3D Reconstruction 1.1.2 Underground Environments as Challenging Scenario 1.1.3 Improved Mobile Camera Systems for Full Omnidirectional Imaging 1.2 Issues 1.2.1 Directional versus Omnidirectional Image Acquisition 1.2.2 Structure from Motion versus Visual Simultaneous Localization and Mapping 1.3 Contribution 1.4 Structure of this Work 2 Related Work 2.1 Visual Simultaneous Localization and Mapping 2.1.1 Visual Odometry 2.1.2 Pose Graph Optimization 2.2 Structure from Motion 2.2.1 Bundle Adjustment 2.2.2 Structureless Bundle Adjustment 2.3 Corresponding Issues 2.4 Proposed Reconstruction Pipeline 3 Cameras and Pixel-to-Sphere Mappings with P2S-Maps 3.1 Types 3.2 Models 3.2.1 Unified Camera Model 3.2.2 Polynomal Camera Model 3.2.3 Spherical Camera Model 3.3 P2S-Maps - Mapping onto Unit Sphere via Lookup Table 3.3.1 Lookup Table as Color Image 3.3.2 Lookup Interpolation 3.3.3 Depth Data Conversion 4 Calibration 4.1 Overview of Proposed Calibration Pipeline 4.2 Target Detection 4.3 Intrinsic Calibration 4.3.1 Selected Examples 4.4 Extrinsic Calibration 4.4.1 3D-2D Pose Estimation 4.4.2 2D-2D Pose Estimation 4.4.3 Pose Optimization 4.4.4 Uncertainty Estimation 4.4.5 PoseGraph Representation 4.4.6 Bundle Adjustment 4.4.7 Selected Examples 5 Full Omnidirectional Image Projections 5.1 Panoramic Image Stitching 5.2 World Map Projections 5.3 World Map Projection Generator for P2S-Maps 5.4 Conversion between Projections based on P2S-Maps 5.4.1 Proposed Workflow 5.4.2 Data Storage Format 5.4.3 Real World Example 6 Relations between Two Camera Spheres 6.1 Forward and Backward Projection 6.2 Triangulation 6.2.1 Linear Least Squares Method 6.2.2 Alternative Midpoint Method 6.3 Epipolar Geometry 6.4 Transformation Recovery from Essential Matrix 6.4.1 Cheirality 6.4.2 Standard Procedure 6.4.3 Simplified Procedure 6.4.4 Improved Procedure 6.5 Two-View Estimation 6.5.1 Evaluation Strategy 6.5.2 Error Metric 6.5.3 Evaluation of Estimation Algorithms 6.5.4 Concluding Remarks 6.6 Two-View Optimization 6.6.1 Epipolar-Based Error Distances 6.6.2 Projection-Based Error Distances 6.6.3 Comparison between Error Distances 6.7 Two-View Translation Scaling 6.7.1 Linear Least Squares Estimation 6.7.2 Non-Linear Least Squares Optimization 6.7.3 Comparison between Initial and Optimized Scaling Factor 6.8 Homography to Identify Degeneracies 6.8.1 Homography for Spherical Cameras 6.8.2 Homography Estimation 6.8.3 Homography Optimization 6.8.4 Homography and Pure Rotation 6.8.5 Homography in Epipolar Geometry 7 Relations between Three Camera Spheres 7.1 Three View Geometry 7.2 Crossing Epipolar Planes Geometry 7.3 Trifocal Geometry 7.4 Relation between Trifocal, Three-View and Crossing Epipolar Planes 7.5 Translation Ratio between Up-To-Scale Two-View Transformations 7.5.1 Structureless Determination Approaches 7.5.2 Structure-Based Determination Approaches 7.5.3 Comparison between Proposed Approaches 8 Pose Graphs 8.1 Optimization Principle 8.2 Solvers 8.2.1 Additional Graph Solvers 8.2.2 False Loop Closure Detection 8.3 Pose Graph Generation 8.3.1 Generation of Synthetic Pose Graph Data 8.3.2 Optimization of Synthetic Pose Graph Data 9 Structureless Camera Motion Estimation 9.1 SCME Pipeline 9.2 Determination of Two-View Translation Scale Factors 9.3 Integration of Depth Data 9.4 Integration of Extrinsic Camera Constraints 10 Camera Motion Estimation Results 10.1 Directional Camera Images 10.2 Omnidirectional Camera Images 11 Conclusion 11.1 Summary 11.2 Outlook and Future Work Appendices A.1 Additional Extrinsic Calibration Results A.2 Linear Least Squares Scaling A.3 Proof Rank Deficiency A.4 Alternative Derivation Midpoint Method A.5 Simplification of Depth Calculation A.6 Relation between Epipolar and Circumferential Constraint A.7 Covariance Estimation A.8 Uncertainty Estimation from Epipolar Geometry A.9 Two-View Scaling Factor Estimation: Uncertainty Estimation A.10 Two-View Scaling Factor Optimization: Uncertainty Estimation A.11 Depth from Adjoining Two-View Geometries A.12 Alternative Three-View Derivation A.12.1 Second Derivation Approach A.12.2 Third Derivation Approach A.13 Relation between Trifocal Geometry and Alternative Midpoint Method A.14 Additional Pose Graph Generation Examples A.15 Pose Graph Solver Settings A.16 Additional Pose Graph Optimization Examples Bibliograph

Medical SLAM in an autonomous robotic system

One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-operative morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilities by observing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted instruments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This thesis addresses the ambitious goal of achieving surgical autonomy, through the study of the anatomical environment by Initially studying the technology present and what is needed to analyze the scene: vision sensors. A novel endoscope for autonomous surgical task execution is presented in the first part of this thesis. Which combines a standard stereo camera with a depth sensor. This solution introduces several key advantages, such as the possibility of reconstructing the 3D at a greater distance than traditional endoscopes. Then the problem of hand-eye calibration is tackled, which unites the vision system and the robot in a single reference system. Increasing the accuracy in the surgical work plan. In the second part of the thesis the problem of the 3D reconstruction and the algorithms currently in use were addressed. In MIS, simultaneous localization and mapping (SLAM) can be used to localize the pose of the endoscopic camera and build ta 3D model of the tissue surface. Another key element for MIS is to have real-time knowledge of the pose of surgical tools with respect to the surgical camera and underlying anatomy. Starting from the ORB-SLAM algorithm we have modified the architecture to make it usable in an anatomical environment by adding the registration of the pre-operative information of the intervention to the map obtained from the SLAM. Once it has been proven that the slam algorithm is usable in an anatomical environment, it has been improved by adding semantic segmentation to be able to distinguish dynamic features from static ones. All the results in this thesis are validated on training setups, which mimics some of the challenges of real surgery and on setups that simulate the human body within Autonomous Robotic Surgery (ARS) and Smart Autonomous Robotic Assistant Surgeon (SARAS) projects

Medical SLAM in an autonomous robotic system

One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-operative morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilities by observing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted instruments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This thesis addresses the ambitious goal of achieving surgical autonomy, through the study of the anatomical environment by Initially studying the technology present and what is needed to analyze the scene: vision sensors. A novel endoscope for autonomous surgical task execution is presented in the first part of this thesis. Which combines a standard stereo camera with a depth sensor. This solution introduces several key advantages, such as the possibility of reconstructing the 3D at a greater distance than traditional endoscopes. Then the problem of hand-eye calibration is tackled, which unites the vision system and the robot in a single reference system. Increasing the accuracy in the surgical work plan. In the second part of the thesis the problem of the 3D reconstruction and the algorithms currently in use were addressed. In MIS, simultaneous localization and mapping (SLAM) can be used to localize the pose of the endoscopic camera and build ta 3D model of the tissue surface. Another key element for MIS is to have real-time knowledge of the pose of surgical tools with respect to the surgical camera and underlying anatomy. Starting from the ORB-SLAM algorithm we have modified the architecture to make it usable in an anatomical environment by adding the registration of the pre-operative information of the intervention to the map obtained from the SLAM. Once it has been proven that the slam algorithm is usable in an anatomical environment, it has been improved by adding semantic segmentation to be able to distinguish dynamic features from static ones. All the results in this thesis are validated on training setups, which mimics some of the challenges of real surgery and on setups that simulate the human body within Autonomous Robotic Surgery (ARS) and Smart Autonomous Robotic Assistant Surgeon (SARAS) projects

Multi-robot Collaborative Visual Navigation with Micro Aerial Vehicles

Micro Aerial Vehicles (MAVs), particularly multi-rotor MAVs have gained significant popularity in the autonomous robotics research field. The small size and agility of these aircraft makes them safe to use in contained environments. As such MAVs have numerous applications with respect to both the commercial and research fields, such as Search and Rescue (SaR), surveillance, inspection and aerial mapping. In order for an autonomous MAV to safely and reliably navigate within a given environment the control system must be able to determine the state of the aircraft at any given moment. The state consists of a number of extrinsic variables such as the position, velocity and attitude of the MAV. The most common approach for outdoor operations is the Global Positioning System (GPS). While GPS has been widely used for long range navigation in open environments, its performance degrades significantly in constrained environments and is unusable indoors. As a result state estimation for MAVs in such constrained environments is a popular and exciting research area. Many successful solutions have been developed using laser-range finder sensors. These sensors provide very accurate measurements at the cost of increased power and weight requirements. Cameras offer an attractive alternative state estimation sensor; they offer high information content per image coupled with light weight and low power consumption. As a result much recent work has focused on state estimation on MAVs where a camera is the only exteroceptive sensor. Much of this recent work focuses on single MAVs, however it is the author's belief that the full potential and benefits of the MAV platform can only be realised when teams of MAVs are able to cooperatively perform tasks such as SaR or mapping. Therefore the work presented in this thesis focuses on the problem of vision-based navigation for MAVs from a multi-robot perspective. Multi-robot visual navigation presents a number of challenges, as not only must the MAVs be able to estimate their state from visual observations of the environment but they must also be able to share the information they gain about their environment with other members of the team in a meaningful fashion. The meaningful sharing of observations is achieved when the MAVs have a common frame of reference for both positioning and observations. Such meaningful information sharing is key to achieving cooperative multi-robot navigation. In this thesis two main ideas are explored to address these issues. Firstly the idea of appearance based (re)-localisation is explored as a means of establishing a common reference frame for multiple MAVs. This approach allows a team of MAVs to very easily establish a common frame of reference prior to starting their mission. The common reference frame allows all subsequent operations, such as surveillance or mapping, to proceed with direct cooperative between all MAVs. The second idea focuses on the structure and nature of the inter-robot communication with respect to visual navigation; the thesis explores how a partially distributed architecture can be used to vastly improve the scalability and robustness of a multi-MAV visual navigation framework. A navigation framework would not be complete without a means of control. In the multi-robot setting the control problem is complicated by the need for inter-robot collision avoidance. This thesis presents a MAV trajectory controller based on a combination of classical control theory and distributed Velocity Obstacle (VO) based collision avoidance. Once a means of control is established an autonomous multi-MAV team requires a mission. One such mission is the task of exploration; that is exploration of a previously unknown environment in order to produce a map and/or search for objects of interest. This thesis also addressed the problem of multi-robot exploration using only the sparse interest-point data collected from the visual navigation system. In a multi-MAV exploration scenario the problem of task allocation, assigning areas to each MAV to explore, can be a challenging one. An auction-based protocol is considered to address the task allocation problem. The two applications discussed, VO-based trajectory control and auction-based environment exploration, form two case studies which serve as the partial basis of the evaluation of the navigation solutions presented in this thesis. In summary the visual navigation systems presented in this thesis allow MAVs to cooperatively perform task such as collision avoidance and environment exploration in a robust and efficient manner, with large teams of MAVs. The work presented is a step in the direction of fully autonomous teams of MAVs performing complex, dangerous and useful tasks in the real world

Appearance and Geometry Assisted Visual Navigation in Urban Areas

Navigation is a fundamental task for mobile robots in applications such as exploration, surveillance, and search and rescue. The task involves solving the simultaneous localization and mapping (SLAM) problem, where a map of the environment is constructed. In order for this map to be useful for a given application, a suitable scene representation needs to be defined that allows spatial information sharing between robots and also between humans and robots. High-level scene representations have the benefit of being more robust and having higher exchangeability for interpretation. With the aim of higher level scene representation, in this work we explore high-level landmarks and their usage using geometric and appearance information to assist mobile robot navigation in urban areas. In visual SLAM, image registration is a key problem. While feature-based methods such as scale-invariant feature transform (SIFT) matching are popular, they do not utilize appearance information as a whole and will suffer from low-resolution images. We study appearance-based methods and propose a scale-space integrated Lucas-Kanade’s method that can estimate geometric transformations and also take into account image appearance with different resolutions. We compare our method against state-of-the-art methods and show that our method can register images efficiently with high accuracy. In urban areas, planar building facades (PBFs) are basic components of the quasirectilinear environment. Hence, segmentation and mapping of PBFs can increase a robot’s abilities of scene understanding and localization. We propose a vision-based PBF segmentation and mapping technique that combines both appearance and geometric constraints to segment out planar regions. Then, geometric constraints such as reprojection errors, orientation constraints, and coplanarity constraints are used in an optimization process to improve the mapping of PBFs. A major issue in monocular visual SLAM is scale drift. While depth sensors, such as lidar, are free from scale drift, this type of sensors are usually more expensive compared to cameras. To enable low-cost mobile robots equipped with monocular cameras to obtain accurate position information, we use a 2D lidar map to rectify imprecise visual SLAM results using planar structures. We propose a two-step optimization approach assisted by a penalty function to improve on low-quality local minima results. Robot paths for navigation can be either automatically generated by a motion planning algorithm or provided by a human. In both cases, a scene representation of the environment, i.e., a map, is useful to specify meaningful tasks for the robot. However, SLAM results usually produce a sparse scene representation that consists of low-level landmarks, such as point clouds, which are neither convenient nor intuitive to use for task specification. We present a system that allows users to program mobile robots using high-level landmarks from appearance data

Robust Visual Odometry and Dynamic Scene Modelling

Image-based estimation of camera trajectory, known as visual odometry (VO), has been a popular solution for robot navigation in the past decade due to its low-cost and widely applicable properties. The problem of tracking self-motion as well as motion of objects in the scene using information from a camera is known as multi-body visual odometry and is a challenging task. The performance of VO is heavily sensitive to poor imaging conditions (i.e., direct sunlight, shadow and image blur), which limits its feasibility in many challenging scenarios. Current VO solutions can provide accurate camera motion estimation in largely static scene. However, the deployment of robotic systems in our daily lives requires systems to work in significantly more complex, dynamic environment. This thesis aims to develop robust VO solutions against two challenging cases, underwater and highly dynamic environments, by extensively analyzing and overcoming the difficulties in both cases to achieve accurate ego-motion estimation. Furthermore, to better understand and exploit dynamic scene information, this thesis also investigates the motion of moving objects in dynamic scene, and presents a novel way to integrate ego and object motion estimation into a single framework. In particular, the problem of VO in underwater is challenging due to poor imaging condition and inconsistent motion caused by water flow. This thesis intensively tests and evaluates possible solutions to the mentioned issues, and proposes a stereo underwater VO system that is able to robustly and accurately localize the autonomous underwater vehicle (AUV). Visual odometry in dynamic environment is challenging because dynamic parts of the scene violate the static world assumption fundamental in most classical visual odometry algorithms. If moving parts of a scene dominate the static scene, off-the-shelf VO systems either fail completely or return poor quality trajectory estimation. Most existing techniques try to simplify the problem by removing dynamic information. Arguably, in most scenarios, the dynamics corresponds to a finite number of individual objects that are rigid or piecewise rigid, and their motions can be tracked and estimated in the same way as the ego-motion. With this consideration, the thesis proposes a brand new way to model and estimate object motion, and introduces a novel multi-body VO system that addresses the problem of tracking of both ego and object motion in dynamic outdoor scenes. Based on the proposed multi-body VO framework, this thesis also exploits the spatial and temporal relationships between the camera and object motions, as well as static and dynamic structures, to obtain more consistent and accurate estimations. To this end, the thesis introduces a novel visual dynamic object-aware SLAM system, that is able to achieve robust multiple moving objects tracking, accurate estimation of full SE(3) object motions, and extract inherent linear velocity information of moving objects, along with an accurate robot localisation and mapping of environment structure. The performance of the proposed system is demonstrated on real datasets, showing its capability to resolve rigid object motion estimation and yielding results that outperform state-of-the-art algorithms by an order of magnitude in urban driving scenarios

Combined Learned and Classical Methods for Real-Time Visual Perception in Autonomous Driving

Autonomy, robotics, and Artificial Intelligence (AI) are among the main defining themes of next-generation societies. Of the most important applications of said technologies is driving automation which spans from different Advanced Driver Assistance Systems (ADAS) to full self-driving vehicles. Driving automation is promising to reduce accidents, increase safety, and increase access to mobility for more people such as the elderly and the handicapped. However, one of the main challenges facing autonomous vehicles is robust perception which can enable safe interaction and decision making. With so many sensors to perceive the environment, each with its own capabilities and limitations, vision is by far one of the main sensing modalities. Cameras are cheap and can provide rich information of the observed scene. Therefore, this dissertation develops a set of visual perception algorithms with a focus on autonomous driving as the target application area. This dissertation starts by addressing the problem of real-time motion estimation of an agent using only the visual input from a camera attached to it, a problem known as visual odometry. The visual odometry algorithm can achieve low drift rates over long-traveled distances. This is made possible through the innovative local mapping approach used. This visual odometry algorithm was then combined with my multi-object detection and tracking system. The tracking system operates in a tracking-by-detection paradigm where an object detector based on convolution neural networks (CNNs) is used. Therefore, the combined system can detect and track other traffic participants both in image domain and in 3D world frame while simultaneously estimating vehicle motion. This is a necessary requirement for obstacle avoidance and safe navigation. Finally, the operational range of traditional monocular cameras was expanded with the capability to infer depth and thus replace stereo and RGB-D cameras. This is accomplished through a single-stream convolution neural network which can output both depth prediction and semantic segmentation. Semantic segmentation is the process of classifying each pixel in an image and is an important step toward scene understanding. Literature survey, algorithms descriptions, and comprehensive evaluations on real-world datasets are presented.Ph.D.College of Engineering & Computer ScienceUniversity of Michiganhttps://deepblue.lib.umich.edu/bitstream/2027.42/153989/1/Mohamed Aladem Final Dissertation.pdfDescription of Mohamed Aladem Final Dissertation.pdf : Dissertatio

Localization in urban environments. A hybrid interval-probabilistic method

Ensuring safety has become a paramount concern with the increasing autonomy of vehicles and the advent of autonomous driving. One of the most fundamental tasks of increased autonomy is localization, which is essential for safe operation. To quantify safety requirements, the concept of integrity has been introduced in aviation, based on the ability of the system to provide timely and correct alerts when the safe operation of the systems can no longer be guaranteed. Therefore, it is necessary to assess the localization's uncertainty to determine the system's operability. In the literature, probability and set-membership theory are two predominant approaches that provide mathematical tools to assess uncertainty. Probabilistic approaches often provide accurate point-valued results but tend to underestimate the uncertainty. Set-membership approaches reliably estimate the uncertainty but can be overly pessimistic, producing inappropriately large uncertainties and no point-valued results. While underestimating the uncertainty can lead to misleading information and dangerous system failure without warnings, overly pessimistic uncertainty estimates render the system inoperative for practical purposes as warnings are fired more often. This doctoral thesis aims to study the symbiotic relationship between set-membership-based and probabilistic localization approaches and combine them into a unified hybrid localization approach. This approach enables safe operation while not being overly pessimistic regarding the uncertainty estimation. In the scope of this work, a novel Hybrid Probabilistic- and Set-Membership-based Coarse and Refined (HyPaSCoRe) Localization method is introduced. This method localizes a robot in a building map in real-time and considers two types of hybridizations. On the one hand, set-membership approaches are used to robustify and control probabilistic approaches. On the other hand, probabilistic approaches are used to reduce the pessimism of set-membership approaches by augmenting them with further probabilistic constraints. The method consists of three modules - visual odometry, coarse localization, and refined localization. The HyPaSCoRe Localization uses a stereo camera system, a LiDAR sensor, and GNSS data, focusing on localization in urban canyons where GNSS data can be inaccurate. The visual odometry module computes the relative motion of the vehicle. In contrast, the coarse localization module uses set-membership approaches to narrow down the feasible set of poses and provides the set of most likely poses inside the feasible set using a probabilistic approach. The refined localization module further refines the coarse localization result by reducing the pessimism of the uncertainty estimate by incorporating probabilistic constraints into the set-membership approach. The experimental evaluation of the HyPaSCoRe shows that it maintains the integrity of the uncertainty estimation while providing accurate, most likely point-valued solutions in real-time. Introducing this new hybrid localization approach contributes to developing safe and reliable algorithms in the context of autonomous driving