Dagmar Lang

Real-time 3D mapping of rough terrain: A field report from Disaster City

Mobile systems for mapping and terrain classification are often tested on datasets of intact environments only. The behavior of the algorithms in unstructured environments is mostly unknown. In safety, security and rescue environments, the robots have to handle much rougher terrain. Therefore, there is a need for 3D test data that also contains disaster scenarios. During the Response Robot Evaluation Exercise in March 2010 in Disaster City, College Station, Texas (USA), a comprehensive dataset was recorded containing the data of a 3D laser range finder, a GPS receiver, an IMU and a color camera. We tested our algorithms (for terrain classification and 3D mapping) with the dataset, and will make the data available to give other researchers the chance to do the same. We believe that this captured data of this well known location provides a valuable dataset for the USAR robotics community, increasing chances of getting more comparable results.

Heat mapping for improved victim detection

Disasters, such as earthquakes or tsunamis, result in destroyed buildings and other dangerous scenarios for human rescuers. Remotely controlled robots are often used to aid humans. Since those robots require steady communication, they are limited to a certain range and might get stuck in case the connection is interrupted. To augment remote controlled robots, autonomous robots are needed. These robots are able to navigate in devastated areas to detect victims while creating a map of the environment at the same time. Victim detection based on thermal sensors is the most widely used approach. In this paper we present a novel approach based on low-cost thermal sensors, using the global heat distribution. Therefore we developed a 2D heat map, which is created by the combination of thermal and laser information during continuous autonomous exploration. The map is build from the history of all sensor readings over time resulting in a heat distribution. The main contribution of this paper is the introduction of a 2D heat map accumulating thermal sensor readings over time for improved victim detection.

Adaptivity of conditional random field based outdoor point cloud classification

In this paper we present how adaptable learned models of graphical models are and how they can be used for classification tasks of 3D laser point clouds with different distributions and density. In order to model the contextual information we use a pair-wise conditional random field and an adaptive graph down-sampling method based on voxel grids. As feature we apply the rotation invariant histogram-of-oriented-residuals operator to describe the local point cloud distribution. We validate the approach with data collected from different laser range finders with varying point cloud distribution and density. Our experiments imply, that conditional random field models learned from one dataset can be applied to another dataset without a significant loss of precision.

Definition of semantic maps for outdoor robotic tasks

In recent years, a lot of work was conducted in order to give robots the ability to gather semantic information about their environment, store it, represent it for the user and to perform high-level tasks based on the semantic information. Most of the systems use internal representations of the gathered information that is not intuitively understandable by humans and that is inadequate for learning from commonly available sources. The combination of object/place classification and common-sense knowledge to semantic maps found its way into indoor semantic mapping approaches to improve human-robot interaction. The aim is to assign complex task settings to the robot allowing it to guide the search for the solution by itself. In this paper, we present a formal common definition of semantic maps. We discuss different criteria for designing and classifying semantic maps and their appropriate challenges. Furthermore, we present an outdoor semantic mapping approach incorporating common-sense knowledge into the classification process and a suitable map representation for high-level tasks.

Markov random field terrain classification of large-scale 3D maps

Simultaneous localization and mapping, drivability classification of the terrain and path planning represent three major research areas in the field of autonomous outdoor robotics. Especially unstructured environments require a careful examination as they are unknown, continuous and the number of possible actions for the robot are infinite. We present an approach to create a semantic 3D map with drivability information for wheeled robots using a terrain classification algorithm. Our robot is equipped with a 3D laser range finder, a Velodyne HDL-64E, as primary sensor. For the registration of the point clouds, we use a featureless 3D correlative scan matching algorithm which is an adaption of the 2D algorithm presented by Olson. Every 3D laser scan is additionally classified with a Markov random field based terrain classification algorithm. Our data structure for the terrain classification approach is a 2D grid whose cells carry information extracted from the laser range finder data. All cells within the grid are classified and their surface is analyzed regarding its drivability for wheeled robots. The main contribution of this work is the novel combination of these two algorithms which yields classified 3D maps with obstacle and drivability information. Thereby, the newly created semantic map is perfectly tailored for generic path planning applications for all kinds of wheeled robots. We evaluate our algorithms on large datasets with more than 137 million annotated 3D points that were labeled by multiple human experts. All datasets are published online and are provided for the community.

Semantic mapping for mobile outdoor robots

In this paper we present the concept and realization of a semantic mapping system for a mobile outdoor robot. Semantic maps aim to give robots the ability to gather semantic information about their environment, to store it, represent it for the user, and to perform high-level tasks based on the semantic information. The map is build by a system integrating the combination of object classification and common-sense knowledge. We validate the proposed semantic map representation on a real-world 3D point cloud dataset. The presented classification approach achieves an overall precision about 96 %. The semantic maps result into a data structure which offers the opportunity to solve complex task settings and can be integrated onto real robotic systems.

Hierarchical loop detection for mobile outdoor robots

Loop closing is a fundamental part of 3D simultaneous localization and mapping (SLAM) that can greatly enhance the quality of long-term mapping. It is essential for the creation of globally consistent maps. Conceptually, loop closing is divided into detection and optimization. Recent approaches depend on a single sensor to recognize previously visited places in the loop detection stage. In this study, we combine data of multiple sensors such as GPS, vision, and laser range data to enhance detection results in repetitively changing environments that are not sufficiently explained by a single sensor. We present a fast and robust hierarchical loop detection algorithm for outdoor robots to achieve a reliable environment representation even if one or more sensors fail.