Martin J. Schuster

Learning organizational principles in human environments

In the context of robotic assistants in human everyday environments, pick and place tasks are beginning to be competently solved at the technical level. The question of where to place objects or where to pick them up from, among other higher-level reasoning tasks, is therefore gaining practical relevance. In this work, we consider the problem of identifying the organizational structure within an environment, i.e. the problem of determining organizational principles that would allow a robot to infer where to best place a particular, previously unseen object or where to reasonably search for a particular type of object given past observations about the allocation of objects to locations in the environment. This problem can be reasonably formulated as a classification task. We claim that organizational principles are governed by the notion of similarity and provide an empirical analysis of the importance of various features in datasets describing the organizational structure of kitchens. For the aforementioned classification tasks, we compare standard classification methods, reaching average accuracies of at least 79% in all scenarios. We thereby show that, in particular, ontology-based similarity measures are well-suited as highly discriminative features. We demonstrate the use of learned models of organizational principles in a kitchen environment on a real robot system, where the robot identifies a newly acquired item, determines a suitable location and then stores the item accordingly.

Stereo-vision based obstacle mapping for indoor/outdoor SLAM

The creation of local and global maps is crucial for (semi-)autonomous operation of mobile robots in previously unknown environments, e.g. during search and rescue missions. We developed an on-board stereo-vision based mapping system, thereby introducing local obstacle maps that can directly be used for fast local obstacle avoidance and path planning. In addition, we designed them to constitute a suitable input to a widely-used simultaneous localization and mapping (SLAM) algorithm. We performed experiments in unknown indoor, unstructured outdoor as well as mixed environments and demonstrated the applicability of our method to camera setups with small as well as wide field of view. In all three scenarios, we achieved a final 2D position error of less than 0.08% of the full trajectory.

Submap matching for stereo-vision based indoor/outdoor SLAM

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.

The LRU Rover for Autonomous Planetary Exploration and Its Success in the SpaceBotCamp Challenge

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. 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.

Distributed stereo vision-based 6D localization and mapping for multi-robot teams: SCHUSTER et al.

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, long-term 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 real-world multi-robot experiments in areas of up 3,000 m2 (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.