Max Pfingsthorn

A Scalable Hybrid Multi-robot SLAM Method for Highly Detailed Maps

Recent successful SLAM methods employ hybrid map representations combining the strengths of topological maps and occupancy grids. Such representations often facilitate multi-agent mapping. In this paper, a successful SLAM method is presented, which is inspired by the manifolddata structure by Howard et al. This method maintains a graph with sensor observations stored in vertices and pose differences including uncertainty information stored in edges. Through its graph structure, updates are local and can be efficiently communicated to peers. The graph links represent known traversable space, and facilitate tasks like path planning. We demonstrate that our SLAM method produces very detailed maps without sacrificing scalability. The presented method was used by the UvA Rescue Virtual Robots team, which won the Best Mapping Award in the RoboCup Rescue Virtual Robots competition in 2006.

Fast 3D mapping by matching planes extracted from range sensor point-clouds

This article addresses fast 3D mapping by a mobile robot in a predominantly planar environment. It is based on a novel pose registration algorithm based entirely on matching features composed of plane-segments extracted from point-clouds sampled from a 3D sensor. The approach has advantages in terms of robustness, speed and storage as compared to the voxel based approaches. Unlike previous approaches, the uncertainty in plane parameters is utilized to compute the uncertainty in the pose computed by scan-registration. The algorithm is illustrated by creating a full 3D model of a multi-level robot testing arena.

Safety, Security, and Rescue Missions with an Unmanned Aerial Vehicle (UAV)

Several missions with an Unmanned Aerial Vehicle (UAV) in different realistic safety, security, and rescue field tests are presented. First, results from two safety and security missions at the 2009 European Land Robot Trials (ELROB) are presented. A UAV in form of an Airrobot AR100-B is used in a reconnaissance and in a camp security scenario. The UAV is capable of autonomous waypoint navigation using onboard GPS processing. A digital video stream from the vehicle is used to create photo maps—also known as mosaicking—in real time at the operator station. This mapping is done using an enhanced version of Fourier Mellin based registration, which turns out to be very fast and robust. Furthermore, results from a rescue oriented scenario at the 2010 Response Robot Evaluation Exercises (RREE) at Disaster City, Texas are presented. The registration for the aerial mosaicking is supplemented by an uncertainty metric and embedded into Simultaneous Localization and Mapping (SLAM), which further enhances the photo maps as main mission deliveries.

Maximum likelihood mapping with spectral image registration

A core challenge in probabilistic mapping is to extract meaningful uncertainty information from data registration methods. While this has been investigated in ICP-based scan matching methods, other registration methods have not been analyzed. In this paper, an uncertainty analysis of a Fourier Mellin based image registration algorithm is introduced, which to our knowledge is the first of its kind involving spectral registration. A covariance matrix is extracted from the result of a Phase-Only Matched Filter, which is interpreted as a probability mass function. The method is embedded in a pose graph implementation for Simultaneous Localization and Mapping (SLAM) and validated with experiments in the underwater domain.

Determining Map Quality through an Image Similarity Metric

A quantitative assessment of the quality of a robot generated map is of high interest for many reasons. First of all, it allows individual researchers to quantify the quality of their mapping approach and to study the effects of system specific choices like different parameter values in an objective way. Second, it allows peer groups to rank the quality of their different approaches to determine scientific progress; similarly, it allows rankings within competition environments like RoboCup. A quantitative assessment of map quality based on an image similarity metric ? is introduced here. It is shown through synthetic as well as through real world data that the metric captures intuitive notions of map quality. Furthermore, the metric is compared to a seemingly more straightforward metric based on Least Mean Squared Euclidean distances (LMS-ED) between map points and ground truth. It is shown that both capture intuitive notions of map quality in a similar way, but that ? can be computed much more efficiently than the LMS-ED.

Augmented autonomy: Improving human-robot team performance in Urban search and rescue

Exploration of unknown environments remains one of the fundamental problems of mobile robotics. It is also a prime example for a task that can benefit significantly from multi-robot teams. We present an integrated system for semi-autonomous cooperative exploration, augmented by an intuitive user interface for efficient human supervision and control. In this preliminary study we demonstrate the effectiveness of the system as a whole and the intuitive interface in particular. Congruent with previous findings, results confirm that having a human in the loop improves task performance, especially with larger numbers of robots. Specific to our interface, we find that even untrained operators can efficiently manage a decently sized team of robots.

Simultaneous localization and mapping with multimodal probability distributions

Simultaneous Localization and Mapping (SLAM) has focused on noisy but unique data associations resulting in linear Gaussian uncertainty models. However, a unique decision is often not possible using only local information, giving rise to ambiguities that have to be resolved globally during optimization. To solve this problem, the pose graph data structure is extended here by multimodal constraints modeled by mixtures of Gaussians (MoG). Furthermore, optimization methods for this novel formulation are introduced, namely (a) robust iteratively reweighted least squares, and (b) Prefilter Stochastic Gradient Descent (SGD) where a preprocessing step determines globally consistent modes before applying SGD. In addition, a variant of the Prefilter method (b) is introduced in form of (c) Prefilter Levenberg-Marquardt. The methods are compared with traditional state-of-the-art optimization methods including (d) Stochastic Gradient Descent and (e) Levenberg-Marquardt as well as (f) Particle filter SLAM and with (g) an optimal exhaustive algorithm. Experiments show that ambiguities significantly impact state-of-the-art methods, and that the novel Prefilter methods (b) and (c) perform best. This is further substantiated with experiments using real-world data. To this end, a method to generate MoG constraints from a plane-based registration algorithm is introduced and used for 3D SLAM under ambiguities.

Efficiently communicating map updates with the pose graph

Robot mapping is a task that can benefit a lot from cooperative multi-robot systems. In multi-robot simultaneous localization and mapping (SLAM), it becomes very important how efficiently a given map can be shared among the robot team. To this end, the recently proposed pose graph map representation is used, adapted for use in a particle filter based mapping algorithm, and compared to the standard occupancy grid representation. Through analysis of corner cases as well as experiments with real robot data, the two map representations are thoroughly compared. It is shown that the pose graph representation allows for much more efficient communication of map updates than the standard occupancy grid.

Uncertainty estimation for a 6-DoF spectral registration method as basis for sonar-based underwater 3D SLAM

An uncertainty estimation method for 6 degree of freedom (6-DoF) spectral registration is introduced here. The underlying 6-DoF registration method based on Phase Only Matched Filtering (POMF) is capable of dealing with very noisy sensor data. It is hence well suited for 3D underwater mapping, where relatively inaccurate sonar imaging devices have to be employed. An uncertainty estimation method is required to use this registration method in a Simultaneous Localization and Mapping (SLAM) framework. To our knowledge, the first such method for 6-DoF spectral registration is presented here. This new uncertainty estimation method treats the POMF results as probability mass functions (PMF). Due to the decoupling in the underlying method, yaw is computed by a one-dimensional POMF leading hence to a 1D PMF; roll and pitch are simultaneously computed and hence encoded in a 2D PMF. Furthermore, a 3D PMF is generated for the translation as it is determined by a 3D POMF. A normal distribution is fitted on each of the PMF to get the uncertainty estimate. The method is experimentally evaluated with simulated as well as real world sonar data. It is shown that it indeed can be used for SLAM, which significantly improves the map quality.

An efficient strategy for data exchange in multi-robot mapping under underwater communication constraints

The online generation of underwater image maps or mosaicking is of high interest for underwater robots, e.g., for autonomous navigation, exploration, or object detection. Here, a cooperative approach is presented that addresses the particular challenges of the severe constraints on communication bandwidth in the underwater domain. Concretely, a special update strategy for a cooperatively maintained pose graph as basis for Simultaneous Localization and Mapping (SLAM) is introduced. The strategy tries to transmit the most relevant information within the limits of the communication bandwidth to maximize the quality of the cooperative map. It is shown in experiments with simulations based on real world data that the strategy leads to near optimal results while obeying the severe bandwidth constraints of realistic underwater communication.