Patrick Pfaff

Multi-Level Surface Maps for Outdoor Terrain Mapping and Loop Closing

To operate outdoors or on non-flat surfaces, mobile robots need appropriate data structures that provide a compact representation of the environment and at the same time support important tasks such as path planning and localization. One such representation that has been frequently used in the past are elevation maps which store in each cell of a discrete grid the height of the surface in the corresponding area. Whereas elevation maps provide a compact representation, they lack the ability to represent vertical structures or even multiple levels. In this paper, we propose a new representation denoted as multi-level surface maps (MLS maps). Our approach allows to store multiple surfaces in each cell of the grid. This enables a mobile robot to model environments with structures like bridges, underpasses, buildings or mines. Additionally, they allow to represent vertical structures. Throughout this paper we present algorithms for updating these maps based on sensory input, to match maps calculated from two different scans, and to solve the loop-closing problem given such maps. Experiments carried out with a real robot in an outdoor environment demonstrate that our approach is well-suited for representing large-scale outdoor environments

Most likely heteroscedastic Gaussian process regression

This paper presents a novel Gaussian process (GP) approach to regression with input-dependent noise rates. We follow Goldberg et al.s approach and model the noise variance using a second GP in addition to the GP governing the noise-free output value. In contrast to Goldberg et al., however, we do not use a Markov chain Monte Carlo method to approximate the posterior noise variance but a most likely noise approach. The resulting model is easy to implement and can directly be used in combination with various existing extensions of the standard GPs such as sparse approximations. Extensive experiments on both synthetic and real-world data, including a challenging perception problem in robotics, show the effectiveness of most likely heteroscedastic GP regression.

Efficient estimation of accurate maximum likelihood maps in 3D

Learning maps is one of the fundamental tasks of mobile robots. In the past, numerous efficient approaches to map learning have been proposed. Most of them, however, assume that the robot lives on a plane. In this paper, we consider the problem of learning maps with mobile robots that operate in non-flat environments and apply maximum likelihood techniques to solve the graph-based SLAM problem. Due to the non-commutativity of the rotational angles in 3D, major problems arise when applying approaches designed for the two-dimensional world. The non-commutativity introduces serious difficulties when distributing a rotational error over a sequence of poses. In this paper, we present an efficient solution to the SLAM problem that is able to distribute a rotational error over a sequence of nodes. Our approach applies a variant of gradient descent to solve the error minimization problem. We implemented our technique and tested it on large simulated and real world datasets. We furthermore compared our approach to solving the problem by LU-decomposition. As the experiments illustrate, our technique converges significantly faster to an accurate map with low error and is able to correct maps with bigger noise than existing methods.

An Efficient Extension to Elevation Maps for Outdoor Terrain Mapping and Loop Closing

Elevation maps are a popular data structure for representing the environment of a mobile robot operating outdoors or on not-flat surfaces. Elevation maps store in each cell of a discrete grid the height of the surface at the corresponding place in the environment. However, the use of this 2½-dimensional representation, is disadvantageous when utilized for mapping with mobile robots operating on the ground, since vertical or overhanging objects cannot be represented appropriately. Furthermore, such objects can lead to registration errors when two elevation maps have to be matched. In this paper, an approach is proposed that allows a mobile robot to deal with vertical and overhanging objects in elevation maps. The approach classifies the points in the environment according to whether they correspond to such objects or not. Also presented is a variant of the ICP algorithm that utilizes the classification of cells during the data association. Additionally, it is shown how the constraints computed by the ICP algorithm can be applied to determine globally consistent alignments. Experiments carried out with a real robot in an outdoor environment demonstrate that the proposed approach yields highly accurate elevation maps even in the case of loops. Experimental results are presented demonstrating that that the proposed classification increases the robustness of the scan matching process.

On the position accuracy of mobile robot localization based on particle filters combined with scan matching

Many applications in mobile robotics and especially industrial applications require that the robot has a precise estimate about its pose. In this paper, we analyze the accuracy of an integrated laser-based robot pose estimation and positioning system for mobile platforms. For our analysis, we used a highly accurate motion capture system to precisely determine the error in the robots pose. We are able to show that by combining standard components such as Monte-Carlo localization, KLD sampling, and scan matching, an accuracy of a few millimeters at taught-in reference locations can be achieved. We believe that this is an important analysis for developers of robotic applications in which pose accuracy matters.

TOURBOT and WebFAIR: Web-operated mobile robots for tele-presence in populated exhibitions

This paper presents a number of techniques that are needed for realizing Web-operated mobile robots. These techniques include effective map-building capabilities, a method for obstacle avoidance based on a combination of range and visual information, and advanced Web and onboard robot interfaces. In addition to video streams, the system provides high-resolution virtual reality visualizations that also include the people in the vicinity of the robot. This increases the flexibility of the interface and simultaneously allows a user to understand the navigation actions of the robot. The techniques described in this article have been successfully deployed within the EU-funded projects TOURBOT and WebFAIR, which aimed to develop interactive tour-guided robots able to serve Web as well as on-site visitors. Technical developments in the framework of these projects have resulted in robust and reliable systems that have been demonstrated and validated in real-world conditions. Equally important, the system setup time has been drastically reduced, facilitating its porting to new environments.

Towards Mapping of Cities

Map learning is a fundamental task in mobile robotics because maps are required for a series of high level applications. In this paper, we address the problem of building maps of large-scale areas like villages or small cities. We present our modified car-like robot which we use to acquire the data about the environment. We introduce our localization system which is based on an information filter and is able to merge the information obtained by different sensors. We furthermore describe out mapping technique that is able to compactly model three-dimensional scenes and allows us efficient and accurate incremental map learning. We additionally apply a global optimization techniques in order to accurately close loops in the environment. Our approach has been implemented and deeply tested on a real car equipped with a series of sensors. Experiments described in this paper illustrate the accuracy and efficiency of the presented techniques.

Gaussian Beam Processes: A Nonparametric Bayesian Measurement Model for Range Finders

In probabilistic mobile robotics, the development of measurement models plays a crucial role as it directly influences the efficiency and the robustness of the robot’s performance in a great variety of tasks including localization, tracking, and map building. In this paper, we present a novel probabilistic measurement model for range finders, called Gaussian beam processes, which treats the measurement modeling task as a nonparametric Bayesian regression problem and solves it using Gaussian processes. The major benefit of our approach is its ability to generalize over entire range scans directly. This way, we can learn the distributions of range measurements for whole regions of the robot’s configuration space from only few recorded or simulated range scans. Especially in approximative approaches to state estimation like particle filtering or histogram filtering, this leads to a better approximation of the true likelihood function. Experiments on real world and synthetic data show that Gaussian beam processes combine the advantages of two popular measurement models.

Robust Monte-Carlo localization using adaptive likelihood models

In probabilistic mobile robot localization, the development of the sensor model plays a crucial role as it directly influences the efficiency and the robustness of the localization process. Sensor models developed for particle filters compute the likelihood of a sensor measurement by assuming that one of the particles accurately represents the true location of the robot. In practice, however, this assumption is often strongly violated, especially when using small sample sets or during global localization. In this paper we introduce a novel, adaptive sensor model that explicitly takes the limited representational power of particle filters into account. As a result, our approach uses smooth likelihood functions during global localization and more peaked functions during position tracking. Experiments show that our technique significantly outperforms existing, static sensor models.

An Efficient Extension of Elevation Maps for Outdoor Terrain Mapping

Elevation maps are a popular data structure for representing the environment of a mobile robot operating outdoors or on not-flat surfaces. Elevation maps store in each cell of a discrete grid the height of the surface the corresponding place in the environment. The use of this \( 2\tfrac{1} {2}\)-dimensional representation, however, is disadvantageous when it is used for mapping with mobile robots operating on the ground, since vertical or overhanging objects cannot be represented appropriately. Such objects furthermore can lead to registration errors when two elevation maps have to be matched. In this paper we propose an approach that allows a mobile robot to deal with vertical and overhanging objects in elevation maps. We classify the points in the environment according to whether they correspond to such objects or not. We also describe a variant of the ICP algorithm that utilizes the classification of cells during the data association. Experiments carried out with a real robot in an outdoor environment demonstrate that the scan matching process becomes significantly more reliable and accurate when our classification is used.