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Joint simultaneous localization and mapping (SLAM) constitutes the basis for cooperative action in multi-robot teams. We designed a stereo vision-based 6D SLAM system combining local and global methods to benefit from their particular advantages: (1) Decoupled local reference filters on each robot for real-time, longterm stable state estimation required for stabilization, control and fast obstacle avoidance; (2) Online graph optimization with a novel graph topology and intra-as well as inter-robot loop closures through an improved submap matching method to provide global multi-robot pose and map estimates; (3) Distribution of the processing of high-frequency and high-bandwidth measurements enabling the exchange of aggregated and thus compacted map data. As a result, we gain robustness with respect to communication losses between robots. We evaluated our improved map matcher on simulated and real-world datasets and present our full system in five realworld multi-robot experiments in areas of up 3,000 m 2 (bounding box), including visual robot detections and submap matches as loop-closure constraints. Further, we demonstrate its application to autonomous multi-robot exploration in a challenging rough-terrain environment at a Moon-analogue site located on a volcano. K E Y W O R D S graph SLAM, map matching, mobile robots, multi-robot, navigation filter 1 | INTRODUCTION The exploration of moons and foreign planets is an important current and future application for mobile robots as their surfaces are difficult to reach and hard to access for humans. The application of huge and complex robot systems such as Curiosity, landed on Mars in 2012, creates many single points of failure for a mission. As a consequence, these rovers have to move very slowly and carefully to avoid getting stuck, as the Mars rover Spirit did in 2009 (Wolchover, 2011). The future deployment of teams of multiple robots can avoid these single points of failure by gaining robustness through redundancy and, in addition, can improve efficiency through parallelization. The robots have to travel through previously unknown unstructured rough terrain, operating in areas where external methods for localization like global navigation satellite systems (GNSS) are not available or expensive to set up. Communication links to the robots are limited and heavily delayed, featuring for example 8-40 min round trip time between Earth andMars. Furthermore, communication between the robots cannot be guaranteed at all times, in particular at scientifically interesting places such as craters, canyons, or caves. As teleoperation therefore becomes inefficient or infeasible, robot autonomy is a key aspect for future planetary exploration missions. Any coordinated (semi-)autonomous operation in such challenging environments requires up-to-date localization estimates for all robots in a team as well as a joint map to operate on.

Teams of mobile robots will play a crucial role in future missions to explore the surfaces of extraterrestrial bodies. Setting up infrastructure and taking scientific samples are expensive tasks when operating in distant, challenging, and unknown environments. In contrast to current single-robot space missions, future heterogeneous robotic teams will increase efficiency via enhanced autonomy and parallelization, improve robustness via functional redundancy, as well as benefit from complementary capabilities of the individual robots. In this article, we present our heterogeneous robotic team, consisting of flying and driving robots that we plan to deploy on scientific sampling demonstration missions at a Moon-analogue site on Mt. Etna, Sicily, Italy in 2021 as part of the ARCHES project. We describe the robots' individual capabilities and their roles in two mission scenarios. We then present components and experiments on important tasks therein: automated task planning, high-level mission control, spectral rock analysis, radio-based localization, collaborative multi-robot 6D SLAM in Moon-analogue and Marslike scenarios, and demonstrations of autonomous sample return.

Teams of mobile robots can be deployed in search and rescue missions to explore previously unknown environments. Methods for joint localization and mapping constitute the basis for (semi-)autonomous cooperative action, in particular when navigating in GPS-denied areas. As communication losses may occur, a decentralized solution is required. With these challenges in mind, we designed a submap-based SLAM system that relies on inertial measurements and stereo-vision to create multi-robot dense 3D maps. For online pose and map estimation, we integrate the results of keyframe-based local reference filters through incremental graph SLAM. To the best of our knowledge, we are the first to combine these two methods to benefit from their particular advantages for 6D multi-robot localization and mapping: Local reference filters on each robot provide real-time, long-term stable state estimates that are required for stabilization, control and fast obstacle avoidance, whereas online graph optimization provides global multi-robot pose and map estimates needed for cooperative planning. We propose a novel graph topology for a decoupled integration of local filter estimates from multiple robots into a SLAM graph according to the filters' uncertainty estimates and independence assumptions and evaluated its benefits on two different robots in indoor, outdoor and mixed scenarios. Further, we performed two extended experiments in a multi-robot setup to evaluate the full SLAM system, including visual robot detections and submap matches as inter-robot loop closure constraints.

The task of planetary exploration poses many challenges for a robot system, from weight and size constraints to sensors and actuators suitable for extraterrestrial environment conditions. As there is a significant communication delay to other planets, the efficient operation of a robot system requires a high level of autonomy. In this work, we present the Light Weight Rover Unit (LRU), a small and agile rover prototype that we designed for the challenges of planetary exploration. Its locomotion system with individually steered wheels allows for high maneuverability in rough terrain and the application of stereo cameras as its main sensor ensures the applicability to space missions. We implemented software components for self-localization in GPS-denied environments, environment mapping, object search and localization and for the autonomous pickup and assembly of objects with its arm. Additional high-level mission control components facilitate both autonomous behavior and remote monitoring of the system state over a delayed communication link. We successfully demonstrated the autonomous capabilities of our LRU at the SpaceBotCamp challenge, a national robotics contest with focus on autonomous planetary exploration. A robot had to autonomously explore a moon-like rough-terrain environment, locate and collect two objects and assemble them after transport to a third object-which the LRU did on its first try, in half of the time and fully autonomous.

Autonomous robots operating in semi-or unstructured environments, e.g. during search and rescue missions, require methods for online on-board creation of maps to support path planning and obstacle avoidance. Perception based on stereo cameras is well suited for mixed indoor/outdoor environments. The creation of full 3D maps in GPS-denied areas however is still a challenging task for current robot systems, in particular due to depth errors resulting from stereo reconstruction. State-of-the-art 6D SLAM approaches employ graph-based optimization on the relative transformations between keyframes or local submaps. To achieve loop closures, correct data association is crucial, in particular for sensor input received at different points in time. In order to approach this challenge, we propose a novel method for submap matching. It is based on robust keypoints, which we derive from local obstacle classification. By describing geometrical 3D features, we achieve invariance to changing viewpoints and varying light conditions. We performed experiments in indoor, outdoor and mixed environments. In all three scenarios we achieved a final 3D position error of less than 0.23% of the full trajectory. In addition, we compared our approach with a 3D RBPF SLAM from previous work, achieving an improvement of at least 27% in mean 2D localization accuracy in different scenarios.

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The evaluation of diagnostics and therapies includes more and more subjective, i.e. emotional and social aspects. Focussing on the handicap experienced by dysphonic patients, the Voice Handicap Index (VHI) has previously been found to be of significant clinical and scientific value for different voices. In this study the VHI questionnaire was applied to demonstrate the voice handicap of 20 male laryngectomees using tracheoesophageal voice (Provox^®), aged 65.5 ± 8.7 years. Their VHI was 45.5 ± 24.1, which was significantly higher than the score of patients with functional voice disorders, but differed only slightly from patients with organic laryngeal dysphonia. Focussing on individual data, VHI scores ranged from values similar to persons without voice disorder to maximum handicap of 101. Comparing the VHI scores with the laryngectomees’ gradual self-perception of voice disorder severity, no consistent relationship was found. Considering the large interindividual differences, the VHI may serve as a valuable instrument for the assessment of individual interventional needs rather than for the identification of a general laryngectomees’ handicap.