Predicting the Next Best View for 3D Mesh Refinement

3D reconstruction is a core task in many applications such as robot navigation or sites inspections. Finding the best poses to capture part of the scene is one of the most challenging topic that goes under the name of Next Best View. Recently, many volumetric methods have been proposed; they choose the Next Best View by reasoning over a 3D voxelized space and by finding which pose minimizes the uncertainty decoded into the voxels. Such methods are effective, but they do not scale well since the underlaying representation requires a huge amount of memory. In this paper we propose a novel mesh-based approach which focuses on the worst reconstructed region of the environment mesh. We define a photo-consistent index to evaluate the 3D mesh accuracy, and an energy function over the worst regions of the mesh which takes into account the mutual parallax with respect to the previous cameras, the angle of incidence of the viewing ray to the surface and the visibility of the region. We test our approach over a well known dataset and achieve state-of-the-art results.Comment: 13 pages, 5 figures, to be published in IAS-1

Autonomous Navigation in Complex Indoor and Outdoor Environments with Micro Aerial Vehicles

Micro aerial vehicles (MAVs) are ideal platforms for surveillance and search and rescue in confined indoor and outdoor environments due to their small size, superior mobility, and hover capability. In such missions, it is essential that the MAV is capable of autonomous flight to minimize operator workload. Despite recent successes in commercialization of GPS-based autonomous MAVs, autonomous navigation in complex and possibly GPS-denied environments gives rise to challenging engineering problems that require an integrated approach to perception, estimation, planning, control, and high level situational awareness. Among these, state estimation is the first and most critical component for autonomous flight, especially because of the inherently fast dynamics of MAVs and the possibly unknown environmental conditions. In this thesis, we present methodologies and system designs, with a focus on state estimation, that enable a light-weight off-the-shelf quadrotor MAV to autonomously navigate complex unknown indoor and outdoor environments using only onboard sensing and computation. We start by developing laser and vision-based state estimation methodologies for indoor autonomous flight. We then investigate fusion from heterogeneous sensors to improve robustness and enable operations in complex indoor and outdoor environments. We further propose estimation algorithms for on-the-fly initialization and online failure recovery. Finally, we present planning, control, and environment coverage strategies for integrated high-level autonomy behaviors. Extensive online experimental results are presented throughout the thesis. We conclude by proposing future research opportunities

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)

Scouting algorithms for field robots using triangular mesh maps

Labor shortage has prompted researchers to develop robot platforms for agriculture field scouting tasks. Sensor-based automatic topographic mapping and scouting algorithms for rough and large unstructured environments were presented. It involves moving an image sensor to collect terrain and other information and concomitantly construct a terrain map in the working field. In this work, a triangular mesh map was first used to represent the rough field surface and plan exploring strategies. A 3D image sensor model was used to simulate collection of field elevation information.A two-stage exploring policy was used to plan the next best viewpoint by considering both the distance and elevation change in the cost function. A greedy exploration algorithm based on the energy cost function was developed; the energy cost function not only considers the traveling distance, but also includes energy required to change elevation and the rolling resistance of the terrain. An information-based exploration policy was developed to choose the next best viewpoint to maximise the information gain and minimize the energy consumption. In a partially known environment, the information gain was estimated by applying the ray tracing algorithm. The two-part scouting algorithm was developed to address the field sampling problem; the coverage algorithm identifies a reasonable coverage path to traverse sampling points, while the dynamic path planning algorithm determines an optimal path between two adjacent sampling points.The developed algorithms were validated in two agricultural fields and three virtual fields by simulation. Greedy exploration policy, based on energy consumption outperformed other pattern methods in energy, time, and travel distance in the first 80% of the exploration task. The exploration strategy, which incorporated the energy consumption and the information gain with a ray tracing algorithm using a coarse map, showed an advantage over other policies in terms of the total energy consumption and the path length by at least 6%. For scouting algorithms, line sweeping methods require less energy and a shorter distance than the potential function method

Learning in Real-Time Search: A Unifying Framework

Real-time search methods are suited for tasks in which the agent is interacting with an initially unknown environment in real time. In such simultaneous planning and learning problems, the agent has to select its actions in a limited amount of time, while sensing only a local part of the environment centered at the agents current location. Real-time heuristic search agents select actions using a limited lookahead search and evaluating the frontier states with a heuristic function. Over repeated experiences, they refine heuristic values of states to avoid infinite loops and to converge to better solutions. The wide spread of such settings in autonomous software and hardware agents has led to an explosion of real-time search algorithms over the last two decades. Not only is a potential user confronted with a hodgepodge of algorithms, but he also faces the choice of control parameters they use. In this paper we address both problems. The first contribution is an introduction of a simple three-parameter framework (named LRTS) which extracts the core ideas behind many existing algorithms. We then prove that LRTA*, epsilon-LRTA*, SLA*, and gamma-Trap algorithms are special cases of our framework. Thus, they are unified and extended with additional features. Second, we prove completeness and convergence of any algorithm covered by the LRTS framework. Third, we prove several upper-bounds relating the control parameters and solution quality. Finally, we analyze the influence of the three control parameters empirically in the realistic scalable domains of real-time navigation on initially unknown maps from a commercial role-playing game as well as routing in ad hoc sensor networks

Next-Best Stereo: Extending Next-Best View Optimisation For Collaborative Sensors

This is the final version of the article. Available from the publisher via the DOI in this record.Most 3D reconstruction approaches passively optimise over all data, exhaustively matching pairs, rather than actively selecting data to process. This is costly both in terms of time and computer resources, and quickly becomes intractable for large datasets. This work proposes an approach to intelligently filter large amounts of data for 3D reconstructions of unknown scenes using monocular cameras. Our contributions are twofold: First, we present a novel approach to efficiently optimise the Next-Best View (NBV) in terms of accuracy and coverage using partial scene geometry. Second, we extend this to intelligently selecting stereo pairs by jointly optimising the baseline and vergence to find the NBV’s best stereo pair to perform reconstruction. Both contributions are extremely efficient, taking 0.8ms and 0.3ms per pose, respectively. Experimental evaluation shows that the proposed method allows efficient selection of stereo pairs for reconstruction, such that a dense model can be obtained with only a small number of images. Once a complete model has been obtained, the remaining computational budget is used to intelligently refine areas of uncertainty, achieving results comparable to state-of-the-art batch approaches on the Middlebury dataset, using as little as 3.8% of the views.The presentation of this paper was made possible by the BMVC 2016 student bursar

Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved

Applying Frontier Cells Based Exploration and Lazy Theta* Path Planning over Single Grid-Based World Representation for Autonomous Inspection of Large 3D Structures with an UAS

Aerial robots are a promising platform to perform autonomous inspection of infrastructures. For this application, the world is a large and unknown space, requiring light data structures to store its representation while performing autonomous exploration and path planning for obstacle avoidance. In this paper, we combine frontier cells based exploration with the Lazy Theta* path planning algorithm over the same light sparse grid—the octree implementation of octomap. Test-driven development has been adopted for the software implementation and the subsequent automated testing process. These tests provided insight into the amount of iterations needed to generate a path with different voxel configurations. The results for synthetic and real datasets are analyzed having as baseline a regular grid with the same resolution as the maximum resolution of the octree. The number of iterations needed to find frontier cells for exploration was smaller in all cases by, at least, one order of magnitude. For the Lazy Theta* algorithm there was a reduction in the number of iterations needed to find the solution in 75% of the cases. These reductions can be explained both by the existent grouping of regions with the same status and by the ability to confine inspection to the known voxels of the octree.Unión Europea, Horizonte 2020, Marie Sklodowska-Curie 64215Unión Europea, MULTIDRONE H2020-ICT-731667Unión Europea, AEROARMS H2020-ICT-64427

Underwater Exploration and Mapping

This paper analyzes the open challenges of exploring and mapping in the underwater realm with the goal of identifying research opportunities that will enable an Autonomous Underwater Vehicle (AUV) to robustly explore different environments. A taxonomy of environments based on their 3D structure is presented together with an analysis on how that influences the camera placement. The difference between exploration and coverage is presented and how they dictate different motion strategies. Loop closure, while critical for the accuracy of the resulting map, proves to be particularly challenging due to the limited field of view and the sensitivity to viewing direction. Experimental results of enforcing loop closures in underwater caves demonstrate a novel navigation strategy. Dense 3D mapping, both online and offline, as well as other sensor configurations are discussed following the presented taxonomy. Experimental results from field trials illustrate the above analysis.acceptedVersio

PredRecon: A Prediction-boosted Planning Framework for Fast and High-quality Autonomous Aerial Reconstruction

Autonomous UAV path planning for 3D reconstruction has been actively studied in various applications for high-quality 3D models. However, most existing works have adopted explore-then-exploit, prior-based or exploration-based strategies, demonstrating inefficiency with repeated flight and low autonomy. In this paper, we propose PredRecon, a prediction-boosted planning framework that can autonomously generate paths for high 3D reconstruction quality. We obtain inspiration from humans can roughly infer the complete construction structure from partial observation. Hence, we devise a surface prediction module (SPM) to predict the coarse complete surfaces of the target from the current partial reconstruction. Then, the uncovered surfaces are produced by online volumetric mapping waiting for observation by UAV. Lastly, a hierarchical planner plans motions for 3D reconstruction, which sequentially finds efficient global coverage paths, plans local paths for maximizing the performance of Multi-View Stereo (MVS), and generates smooth trajectories for image-pose pairs acquisition. We conduct benchmarks in the realistic simulator, which validates the performance of PredRecon compared with the classical and state-of-the-art methods. The open-source code is released at https://github.com/HKUST-Aerial-Robotics/PredRecon.Comment: Accepted by ICRA2023; Code: https://github.com/HKUST-Aerial-Robotics/PredRecon; Video: https://www.youtube.com/watch?v=ek7yY_FZYA