Christian Dornhege

On measuring the accuracy of SLAM algorithms

In this paper, we address the problem of creating an objective benchmark for evaluating SLAM approaches. We propose a framework for analyzing the results of a SLAM approach based on a metric for measuring the error of the corrected trajectory. This metric uses only relative relations between poses and does not rely on a global reference frame. This overcomes serious shortcomings of approaches using a global reference frame to compute the error. Our method furthermore allows us to compare SLAM approaches that use different estimation techniques or different sensor modalities since all computations are made based on the corrected trajectory of the robot.We provide sets of relative relations needed to compute our metric for an extensive set of datasets frequently used in the robotics community. The relations have been obtained by manually matching laser-range observations to avoid the errors caused by matching algorithms. Our benchmark framework allows the user to easily analyze and objectively compare different SLAM approaches.

Semantic attachments for domain-independent planning systems

Solving real-world problems using symbolic planning often requires a simplified formulation of the original problem, since certain subproblems cannot be represented at all or only in a way leading to inefficiency. For example, manipulation planning may appear as a subproblem in a robotic planning context or a packing problem can be part of a logistics task. In this paper we propose an extension of PDDL for specifying semantic attachments. This allows the evaluation of grounded predicates as well as the change of fluents by externally specified functions. Furthermore, we describe a general schema of integrating semantic attachments into a forward-chaining planner and report on our experience of adding this extension to the planners FF and Temporal Fast Downward. Finally, we present some preliminary experiments using semantic attachments.

A comparison of SLAM algorithms based on a graph of relations

In this paper, we address the problem of creating an objective benchmark for comparing SLAM approaches. We propose a framework for analyzing the results of SLAM approaches based on a metric for measuring the error of the corrected trajectory. The metric uses only relative relations between poses and does not rely on a global reference frame. The idea is related to graph-based SLAM approaches in the sense that it considers the energy needed to deform the trajectory estimated by a SLAM approach to the ground truth trajectory. Our method enables us to compare SLAM approaches that use different estimation techniques or different sensor modalities since all computations are made based on the corrected trajectory of the robot. We provide sets of relative relations needed to compute our metric for an extensive set of datasets frequently used in the SLAM community. The relations have been obtained by manually matching laser-range observations. We believe that our benchmarking framework allows the user an easy analysis and objective comparisons between different SLAM approaches.

Real-time Localization and Elevation Mapping within Urban Search and Rescue Scenarios

Urban Search And Rescue (USAR) is a time critical task. Rescue teams have to explore a large terrain within a short amount of time in order to locate survivors after a disaster. One goal in Rescue ...

Mapping disaster areas jointly: RFID-Coordinated SLAM by Hurnans and Robots

We consider the problem of jointly performing SLAM by humans and robots in Urban Search And Rescue (USAR) scenarios. In this context, SLAM is a challenging task. First, places are hardly re-observable by vision techniques since visibility might be affected by smoke and fire. Second, loop-closure is cumbersome due to the fact that fire fighters will intentionally try to avoid performing loops when facing the reality of emergency response, e.g. while they are searching for victims. Furthermore, there might be places that are only accessible to robots, making it necessary to integrate humans and robots into one team for mapping the area after a disaster. In this paper, we introduce a method for jointly correcting individual trajectories of humans and robots by utilizing RFID technology for data association. Hereby the poses of humans and robots are tracked by PDR (Pedestrian Dead Reckoning) and slippage sensitive odometry, respectively. We conducted extensive experiments with a team of humans and robots within a semi-outdoor environment. Results from these experiments show that the introduced method allows to improve single trajectories based on the joint graph, even if they do not contain any loop.

Behavior maps for online planning of obstacle negotiation and climbing on rough terrain

To autonomously navigate on rough terrain is a challenging problem for mobile robots, requiring the ability to decide whether parts of the environment can be traversed or have to be bypassed, which is commonly known as Obstacle Negotiation (ON). In this paper, we introduce a planning framework that extends ON to the general case, where different types of terrain classes directly map to specific robot skills, such as climbing stairs and ramps. This extension is based on a new concept called behavior maps, which is utilized for the planning and execution of complex skills. Behavior maps are directly generated from elevation maps, i.e. two-dimensional grids storing in each cell the corresponding height of the terrain surface, and a set of skill descriptions. Results from extensive experiments are presented, showing that the method enables the robot to explore successfully rough terrain in real-time, while selecting the optimal trajectory in terms of costs for navigation and skill execution.

A frontier-void-based approach for autonomous exploration in 3d

We consider the problem of an autonomous robot searching for objects in unknown 3d space. Similar to the well known frontier-based exploration in 2d, the problem is to determine a minimal sequence of sensor viewpoints until the entire search space has been explored. We introduce a novel approach that combines the two concepts of voids, which are unexplored volumes in 3d, and frontiers, which are regions on the boundary between voids and explored space. Our approach has been evaluated on a mobile platform using a 5-DOF manipulator searching for victims in a simulated USAR setup. First results indicate the real-world capability and search efficiency of the proposed method.

A Frontier-Void-Based Approach for Autonomous Exploration in 3D

We consider the problem of an autonomous robot searching for objects in unknown 3d space. Similar to the well known frontier-based exploration in 2d, the problem is to determine a minimal sequence of sensor viewpoints until the entire search space has been explored. We introduce a novel approach that combines the two concepts of voids, which are unexplored volumes in 3d, and frontiers, which are regions on the boundary between voids and explored space. Our approach has been evaluated on a mobile platform using a 5-DOF manipulator searching for victims in a simulated USAR setup. First results indicate the real-world capability and search efficiency of the proposed method.

Guiding the Generation of Manipulation Plans by Qualitative Spatial Reasoning

Abstract Manipulation planning is a complex task for robots with a manipulator arm that need to grasp objects in the environment, specifically under narrow spatial conditions restricting the workspace of the robot. A popular approach for generating motion plans is probabilistic roadmap planning. However, the sampling strategy of such planners is usually unguided, and hence may lead to motion plans that seem counterintuitive for a human observer. In this article we present an approach that generates heuristics for the probabilistic sampling strategy from spatial plans that abstract from concrete metric data. These spatial plans describe a free trajectory in the workspace of the robot on a purely qualitative level, i.e., by employing spatial relations from formalisms considered in the domain of Qualitative Spatial and Temporal Reasoning. We discuss how such formalisms and constraint-based reasoning methods can be applied to approximate geometrically feasible motions. The paper is completed by an evaluation of a hybrid planning system in different spatial settings showing that run-times are notably improved when an abstract plan is considered as a guidance heuristic.

Large scale graph-based SLAM using aerial images as prior information

To effectively navigate in their environments and accurately reach their target locations, mobile robots require a globally consistent map of the environment. The problem of learning a map with a mobile robot has been intensively studied in the past and is usually referred to as the simultaneous localization and mapping (SLAM) problem. However, existing solutions to the SLAM problem typically rely on loop-closures to obtain global consistency and do not exploit prior information even if it is available. In this paper, we present a novel SLAM approach that achieves global consistency by utilizing publicly accessible aerial photographs as prior information. Our approach inserts correspondences found between three-dimensional laser range scans and the aerial image as constraints into a graph-based formulation of the SLAM problem. We evaluate our algorithm based on large real-world datasets acquired in a mixed in- and outdoor environment by comparing the global accuracy with state-of-the-art SLAM approaches and GPS. The experimental results demonstrate that the maps acquired with our method show increased global consistency.