Stefan May

Robust 3D-mapping with time-of-flight cameras

Time-of-flight cameras constitute a smart and fast technology for 3D perception but lack in measurement precision and robustness. The authors present a comprehensive approach for 3D environment mapping based on this technology. Imprecision of depth measurements are properly handled by calibration and application of several filters. Robust registration is performed by a novel extension to the Iterative Closest Point algorithm. Remaining registration errors are reduced by global relaxation after loop-closure and surface smoothing. A laboratory ground truth evaluation is provided as well as 3D mapping experiments in a larger indoor environment.

Calibration and registration for precise surface reconstruction with Time-Of-Flight cameras

This paper presents a method for precise surface reconstruction with Time-Of-Flight (TOF) cameras. A novel calibration approach, which simplifies the calibration task and doubles the cameras precision is developed and compared to current calibration methods. Remaining errors are tackled by applying filter and error distributing methods. Thus, a reference object is circumferentially reconstructed with an overall mean precision of approximately 3 mm in translation and 3° in rotation. The presented way of quantification for an achievable reconstruction precision with TOF cameras discloses the potential of localising and grasping objects with robot manipulators. This is a major criteria for the potential analysis of this sensor technology in robotics, which is first demonstrated within this work.

The MACS project: an approach to affordance-inspired robot control

In this position paper, we present an outline of the MACS approach to affordance-inspired robot control. An affordance, a concept from Ecological Psychology, denotes a specific relationship between an animal and its environment. Perceiving an affordance means perceiving an interaction possibility that is specific for the animals perception and action capabilities. Perceiving an affordance does not include appearance-based object recognition, but rather feature-based perception of object functions. The central hypothesis of MACS is that an affordance-inspired control architecture enables a robot to perceive more interaction possibilities than a traditional architecture that relies on appearance-based object recognition alone. We describe how the concept of affordances can be exploited for controlling a mobile robot with manipulation capabilities. Particularly, we will describe how affordance support can be built into robot perception, how learning mechanisms can generate affordance-like relations, how this affordance-related information is represented, and how it can be used by a planner for realizing goal-directed robot behavior. We present both the MACS demonstrator and simulator, and summarize development and experiments that have been performed so far. By interfacing perception and goal-directed action in terms of affordances, we will provide a new way for reasoning and learning to connect with reactive robot control. We will show the potential of this new methodology by going beyond navigation-like tasks towards goal-directed autonomous manipulation in our project demonstrators.

A Generalized 2D and 3D Multi-Sensor Data Integration Approach based on Signed Distance Functions for Multi-Modal Robotic Mapping

This paper describes a data integration approach for arbitrary 2D/3D depth sensing units exploiting assets of the signed distance function. The underlying framework generalizes the KinectFusion approach with an objectoriented model respecting different sensor modalities. For instance, measurements of 2D/3D laser range finders and RGB-D cameras can be integrated into the same representation. Exemplary, an environment is reconstructed with a 3D laser range finder, while adding fine details from objects of interest by closer inspection with an RGB-D sensor. A typical application of this approach is the exploration in rescue environments, where large-scale mapping is performed on the basis of long-range laser range finders while hollows are inspected with lightweight sensors attached to a manipulator arm.

Identification of transparent and specular reflective material in laser scans to discriminate affected measurements for faultless robotic SLAM

Abstract Mapping with laser scanners is the state-of-the-art method applied in service, industrial, medical, and rescue robotics. Although a lot of research has been done, maps still suffer from interferences caused by transparent and specular reflective objects. Glass, mirrors, shiny or translucent surfaces cause erroneous measurements depending on the incident angle of the laser beam. In past experiments the Mirror Detector Approach was implemented to determine such measurements with a multi-echo laser scanner. Recognition values are based on their differences in recorded measurements in regard to the distance of the echoes. This paper describes the research to distinguish between reflective and transparent objects. The implemented Mirror Detector was specifically modified for recognition of said objects for which four experiments were conducted; one experiment to show the map of the original Mirror Detector; two experiments to investigate intensity characteristics based on angle, distance, and material; and one experiment to show an applied discrimination with the extended version of the Mirror Detector, the Reflection Classifier Approach. To verify the results, a comparison with existing models was performed. This study showed that shiny metals, like aluminium, etc., provide significant characteristics, while mirrors are to be characterized by a mixed model of glass and shiny metal. Transparent objects turned out to be challenging because their appearance in the sensor data strongly depends on the background. Nevertheless, these experiments show that discrimination of transparent and reflective materials based on the reflected intensity is possible and feasible.

Multi-robot Localization and Mapping Based on Signed Distance Functions

This publication describes a 2D Simultaneous Localization and Mapping approach applicable to multiple mobile robots. The presented strategy uses data of 2D LIDAR sensors to build a dynamic representation based on Signed Distance Functions. A multi-threaded software architecture performs registration and data integration in parallel allowing for drift-reduced pose estimation of multiple robots. Experiments are provided demonstrating the application with single and multiple robot mapping using simulated data, public accessible recorded data as well as two actual robots operating in a comparably large area.

Fast 3D Perception for Collision Avoidance and SLAM in Domestic Environments

Autonomous service robots that assist in housekeeping, serve as butlers, guide visitors through exhibitions in museums and trade fairs, or provide care to elderly and disabled people could substantially ease everyday life for many people and present an enormous economic potential (Haegele et al., 2001; Pollack et al., 2002; Siegwart et al., 2003). Moreover, regarding the aging society in most industrialized countries the application of service robots in (elderly) health care might not only be helpful but necessary in the future. However, these service robots face the challenging task of operating in real-world indoor and domestic environments. Domestic environments tend to be cluttered, dynamic and populated by humans and domestic animals. In order to adequately react to sudden dynamic changes and avoid collisions, these robots need to be able to constantly acquire and process, in real-time, information about their environment. Furthermore, in order to act in a goal-directed manner, plan actions and navigate effectively, autonomous mobile robots need an internal representation ormap of their environment. Nature and complexity of these representations highly depend on the robot’s task and workspace. When operating in preliminary unknown environments, e.g., when it is unfeasible (or simply uncomfortable) to manually model the environment beforehand, the robot needs to construct an internal environment model on its own. Moreover, in dynamic environments the robot further needs to be able to continuously acquire and integrate new sensory information to update the internal environment model in regions where changes have taken place. As integrating new information into the model (mapping) requires knowledge about the robot’s pose (position and orientation in the environment) and determining the robot’s pose requires a map of the environment, these two problems need to be considered jointly and the problem 4

Detection of Specular Reflections in Range Measurements for Faultless Robotic SLAM

Laser scanners are state-of-the-art devices used for mapping in service, industry, medical and rescue robotics. Although a lot of work has been done in laser-based SLAM, maps still suffer from interferences caused by objects like glass, mirrors and shiny or translucent surfaces. Depending on the surface’s reflectivity, a laser beam is deflected such that returned measurements provide wrong distance data. At certain positions phantom-like objects appear. This paper describes a specular reflectance detection approach applicable to the emerging technology of multi-echo laser scanners in order to identify and filter reflective objects. Two filter stages are implemented. The first filter reduces errors in current scans on the fly. A second filter evaluates a set of laser scans, triggered as soon as a reflective surface has been passed. This makes the reflective surface detection more robust and is used to refine the registered map. Experiments demonstrate the detection and elimination of reflection errors. They show improved localization and mapping in environments containing mirrors and large glass fronts is improved.

Multi-Robot Localization and Mapping Based on Signed Distance Functions

This publication describes a 2D Simultaneous Localization and Mapping approach applicable to multiple mobile robots. The presented strategy uses data of 2D LIDAR sensors to build a dynamic representation based on Signed Distance Functions. Novelties of the approach are a joint map built in parallel instead of occasional merging of smaller maps and the limited drift localization which requires no loop closure detection. A multi-threaded software architecture performs registration and data integration in parallel allowing for drift-reduced pose estimation of multiple robots. Experiments are provided demonstrating the application with single and multiple robot mapping using simulated data, public accessible recorded data, two actual robots operating in a comparably large area as well as a deployment of these units at the Robocup rescue league.

Libra3D: Body weight estimation for emergency patients in clinical environments with a 3D structured light sensor

This paper describes the application of a weight estimation method for emergency patients in clinical environments. The approach applies established algorithms for point cloud processing and filtering to data from a low-cost, structured light sensor. A patients volume is estimated on the basis of their visible front surface. The approach is currently being tested in the workflow of the emergency room at the Universitätsklinikum Erlangen, Germany. Preliminary results show the accuracy of the approach in relation to other conservative means of weight measurements, for example, by physicians and anthropometric measurements.