Algorithms for autonomous robotic exploration usually focus on optimizing time and coverage, often in a greedy fashion. However, obstacle inflation is conservative and might limit mapping capabilities and even prevent the robot from moving through narrow, important places. This letter proposes a method to influence the manner the robot moves in the environment by taking into consideration a user-defined spatial preference formulated in a fragment of signal temporal logic (STL). We propose to guide the motion planning toward minimizing the violation of such preference through a cost function that integrates the quantitative semantics, i.e., robustness of STL. To demonstrate the effectiveness of the proposed approach, we integrate it into the autonomous exploration planner (AEP). Results from simulations and real-world experiments are presented, highlighting the benefits of our approach.

The task of exploration does not end when the robot has covered the entire environment. The world is dynamic and to model this property and to keep the map up to date the robot needs to re-explore. In this work, we present an approach to long-term exploration that builds on prior work on dynamic mapping, volumetric representations of space, and exploration planning. The main contribution of our work is a novel formulation of the information gain function that controls the exploration so that it trades off revisiting highly dynamic areas where changes are very likely with covering the rest of the environment to ensure both coverage and up-to-date estimates of the dynamics. We provide experimental validation of our approach in three different simulated environments.

Autonomous mobile robots have in recent years started to enter households in the form of autonomous vacuum cleaners and lawn mowers. The applicability of more advanced and general purpose service robots is almost endless. That is, robots that can perform a variety of tasks, instead of being specialized for a single task. To this end, there are some fundamental challenges that need to be addressed. One of the key capabilities of an autonomous mobile robot is navigation. To achieve truly autonomous navigation, the robot has to be able to localize itself, plan, execute, and update a path that takes it to its desired location, and to generate a map on-the-fly of its environment if the environment is unknown or changing. This thesis focuses on the latter two of these challenges, planning and mapping. More specifically, we investigate in the scenario where the robot lacks any prior knowledge of the environment, referred to as autonomous exploration.

One of the most important insights throughout the thesis is that these challenges should not be examined in isolation. As these are generally not the main tasks, a truly autonomous mobile robot shall perform; instead, they are necessities to fulfill higher-level tasks. Therefore, aspects such as flexibility and scalability should be regarded higher than simply accomplishing the task as efficiently or quickly as possible.

Another insight, specifically regarding mapping, comes from surveying both consumers, the ones using the maps, and producers, the ones creating the maps. Ideally, a mapping framework should be optimized towards both, as it is pointless creating maps that cannot be used as well as assuming data can be extracted from a map in ways that are unfeasible. However, in existing works this is rare. 

A third insight, specifically regarding exploration, comes from breaking down typical assumptions and simplifications that are generally applied to make the problem tractable. We show that the problem is often formulated such that it leads to unnecessary greedy behavior, where the expected information gain has too high priority. Not only do we show that with a more general formulation we can achieve better results, but also that the information gain is not important from a long-term perspective.

In this thesis, we present a mapping framework as well as an exploration framework. With these frameworks, we show that flexibility and scalability do not necessarily have to come at the cost of efficiency. We contribute the mapping framework, UFOMap, and the exploration framework, UFOExplorer, open-source to the community such that others can further develop and build upon them.

This paper presents a collision avoidance method for tele-operated unmanned aerial vehicles (UAVs). The method is designed to assist the operator at all times, such that the operator can focus solely on the main objectives instead of avoiding obstacles. We restrict the altitude to be fixed in a three dimensional environment to simplify the control and operation of the UAV. The method contributes a number of desired properties not found in other collision avoidance systems for tele-operated UAVs. Our method i) can handle situations where there is no input from the user by actively stopping and proceeding to avoid obstacles, ii) allows the operator to slide between prioritizing staying away from objects and getting close to them in a safe way when so required, and iii) provides for intuitive control by not deviating too far from the control input of the operator. We demonstrate the effectiveness of the method in real world experiments with a physical hexacopter in different indoor scenarios. We also present simulation results where we compare controlling the UAV with and without our method activated.

We propose UFOExplorer, a fast and efficient exploration method that scales well with the environment size. An exploration paradigm driven by map updates is proposed to enable the robot to react quicker and to always move towards the optimal exploration goal. For each map update, a dense graph-based planning structure is updated and extended. The planning structure is then used to generate a path using a simple exploration heuristic, which guides the robot towards the closest exploration goal. The proposed method scales well with the environment size, as the planning cost is amortized when updating and extending the planning structure. The simple exploration heuristic performs on par with the most recent state-of-the-art methods in smaller environments and outperforms them in larger environments, both in terms of exploration speed and computational efficiency. The implementation of the method is made available for future research.

3D models are an essential part of many robotic applications. In applications where the environment is unknown a-priori, or where only a part of the environment is known, it is important that the 3D model can handle the unknown space efficiently. Path planning, exploration, and reconstruction all fall into this category. In this letter we present an extension to OctoMap which we call UFOMap. UFOMap uses an explicit representation of all three states in the map, i.e., unknown, free, and occupied. This gives, surprisingly, a more memory efficient representation. We provide methods that allow for significantly faster insertions into the octree. Furthermore, UFOMap supports fast queries based on occupancy state using so called indicators and based on location by exploiting the octree structure and bounding volumes. This enables real-time colored octree mapping at high resolution (below 1 cm). UFOMap is contributed as a C++ library that can be used standalone but is also integrated into ROS.

For autonomous robots to have a deeper understanding of their environment, non-geometric, or semantic information, is required. However, there is currently a gap between the producers and consumers of dense semantic maps. Where producers are focused on reconstruction accuracy but not usability. We introduce an efficient semantic extension of the octree-based online 3D volumetric mapping framework UFOMap, for storing non-geometric information and to bridge this gap. By using a general semantic representation with a compact and dynamic data structure, our framework is capable of building large-scale semantic maps in real time. Benchmarks show that this semantic representation has more than an order of magnitude lower memory footprint than other approaches and up to three orders of magnitude faster queries of information. For example, UFOMap can construct truly large-scale volumetric semantic maps in real-time at high resolution. The framework is available as a standalone open source repository at https://github.com/UnknownFreeOccupied/ufomap and integrated with the Robot Operating System (ROS).

In map-predictive exploration planning, the aim is to exploit a-priori map information to improve planning for exploration in otherwise unknown environments. The use of map predictions in exploration planning leads to exacerbated greediness, as map predictions allow the planner to defer exploring parts of the environment that have low value, e.g., unfinished corners. This behavior is undesirable, as it leaves holes in the explored space by design. To this end, we propose a scoring function based on inverse covisibility that rewards visiting these low-value parts, resulting in a more cohesive exploration process, and preventing excessive greediness in a map-predictive setting. We examine the behavior of a non-greedy map-predictive planner in a bare-bones simulator, and answer two principal questions: a) how far beyond explored space should a map predictor predict to aid exploration, i.e., is more better; and b) does shortest-path search as the basis for planning, a popular choice, cause greediness. Finally, we show that by thresholding covisibility, the user can trade-off greediness for improved early exploration performance.

Maps play a key role in rapidly developing area of autonomous driving. We survey the literature for different map representations and find that while the world is threedimensional, it is common to rely on 2D map representations in order to meet real-time constraints. We believe that high levels of situation awareness require a 3D representation as well as the inclusion of semantic information. We demonstrate that our recently presented hierarchical 3D grid mapping framework UFOMap meets the real-time constraints. Furthermore, we show how it can be used to efficiently support more complex functions such as calculating the occluded parts of space and accumulating the output from a semantic segmentation network.

In environments like offices, the duration of a robot's navigation between two locations may vary over time. For instance, reaching a kitchen may take more time during lunchtime since the corridors are crowded with people heading the same way. In this work, we address the problem of routing in such environments with tasks expressed in Metric Interval Temporal Logic (MITL)-a rich robot task specification language that allows us to capture explicit time requirements. Our objective is to find a strategy that maximizes the temporal robustness of the robot's MITL task. As the first step towards a solution, we define a Mixed-integer linear programming approach to solving the task planning problem over a Varying Weighted Transition System, where navigation durations are deterministic but vary depending on the time of day. Then, we apply this planner to optimize for MITL temporal robustness in Markov Decision Processes, where the navigation durations between physical locations are uncertain, but the time-dependent distribution over possible delays is known. Finally, we develop a receding horizon planner for Markov Decision Processes that preserves guarantees over MITL temporal robustness. We show the scalability of our planning algorithms in simulations of robotic tasks.

While feature association to a global map has significant benefits, to keep the computations from growing exponentially, most lidar-based odometry and mapping methods opt to associate features with local maps at one voxel scale. Taking advantage of the fact that surfels (surface elements) at different voxel scales can be organized in a tree-like structure, we propose an octree-based global map of multi-scale surfels that can be updated incrementally. This alleviates the need for recalculating, for example, a k-d tree of the whole map repeatedly. The system can also take input from a single or a number of sensors, reinforcing the robustness in degenerate cases. We also propose a point-to-surfel (PTS) association scheme, continuous-time optimization on PTS and IMU preintegration factors, along with loop closure and bundle adjustment, making a complete framework for Lidar-Inertial continuous-time odometry and mapping. Experiments on public and in-house datasets demonstrate the advantages of our system compared to other state-of-the-art methods.

Exploration is an important aspect of robotics, whether it is for mapping, rescue missions, or path planning in an unknown environment. Frontier Exploration planning (FEP) and Receding Horizon Next-Best-View planning (RH-NBVP) are two different approaches with different strengths and weaknesses. FEP explores a large environment consisting of separate regions with ease, but is slow at reaching full exploration due to moving back and forth between regions. RH-NBVP shows great potential and efficiently explores individual regions, but has the disadvantage that it can get stuck in large environments not exploring all regions. In this letter, we present a method that combines both approaches, with FEP as a global exploration planner and RH-NBVP for local exploration. We also present techniques to estimate potential information gain faster, to cache previously estimated gains and to exploit these to efficiently estimate new queries.

In the field of robotics, the point cloud has become an essential map representation. From the perspective of downstream tasks like localization and global path planning, points corresponding to dynamic objects will adversely affect their performance. Existing methods for removing dynamic points in point clouds often lack clarity in comparative evaluations and comprehensive analysis. Therefore, we propose an easy-to-extend unified benchmarking framework for evaluating techniques for removing dynamic points in maps. It includes refactored state-of-art methods and novel metrics to analyze the limitations of these approaches. This enables researchers to dive deep into the underlying reasons behind these limitations. The benchmark makes use of several datasets with different sensor types. All the code and datasets related to our study are publicly available for further development and utilization.