Slawomir Grzonka

A Fully Autonomous Indoor Quadrotor

Recently, there has been increased interest in the development of autonomous flying vehicles. However, as most of the proposed approaches are suitable for outdoor operation, only a few techniques have been designed for indoor environments, where the systems cannot rely on the Global Positioning System (GPS) and, therefore, have to use their exteroceptive sensors for navigation. In this paper, we present a general navigation system that enables a small-sized quadrotor system to autonomously operate in indoor environments. To achieve this, we systematically extend and adapt techniques that have been successfully applied on ground robots. We describe all algorithms and present a broad set of experiments, which illustrate that they enable a quadrotor robot to reliably and autonomously navigate in indoor environments.

Towards a navigation system for autonomous indoor flying

Recently there has been increasing research on the development of autonomous flying vehicles.Whereas most of the proposed approaches are suitable for outdoor operation, only a few techniques have been designed for indoor environments. In this paper we present a general system consisting of sensors and algorithms which enables a small sized flying vehicle to operate indoors. This is done by adapting techniques which have been successfully applied on ground robots. We released our system as open-source with the intention to provide the community with a new framework for building applications for indoor flying robots. We present a set of experiments to validate our system on an open source quadrotor.

A tree parameterization for efficiently computing maximum likelihood maps using gradient descent

In 2006, Olson et al. presented a novel approach to address the graph-based simultaneous localization and mapping problem by applying stochastic gradient descent to minimize the error introduced by constraints. Together with multi-level relaxation, this is one of the most robust and efficient maximum likelihood techniques published so far. In this paper, we present an extension of Olsons algorithm. It applies a novel parameterization of the nodes in the graph that significantly improves the performance and enables us to cope with arbitrary network topologies. The latter allows us to bound the complexity of the algorithm to the size of the mapped area and not to the length of the trajectory as it is the case with both previous approaches. We implemented our technique and compared it to multi-level relaxation and Olsons algorithm. As we demonstrate in simulated and in real world experiments, our approach converges faster than the other approaches and yields accurate maps of the environment.

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.

Efficient people tracking in laser range data using a multi-hypothesis leg-tracker with adaptive occlusion probabilities

We present an approach to laser-based people tracking using a multi-hypothesis tracker that detects and tracks legs separately with Kalman filters, constant velocity motion models, and a multi-hypothesis data association strategy. People are defined as high-level tracks consisting of two legs that are found with little model knowledge. We extend the data association so that it explicitly handles track occlusions in addition to detections and deletions. Additionally, we adapt the corresponding probabilities in a situation-dependent fashion so as to reflect the fact that legs frequently occlude each other. Experimental results carried out with a mobile robot illustrate that our approach can robustly and efficiently track multiple people even in situations of high levels of occlusion.

Learning maps in 3D using attitude and noisy vision sensors

In this paper, we address the problem of learning 3D maps of the environment using a cheap sensor setup which consists of two standard web cams and a low cost inertial measurement unit. This setup is designed for lightweight or flying robots. Our technique uses visual features extracted from the web cams and estimates the 3D location of the landmarks via stereo vision. Feature correspondences are estimated using a variant of the PROSAC algorithm. Our mapping technique constructs a graph of spatial constraints and applies an efficient gradient descent-based optimization approach to estimate the most likely map of the environment. Our approach has been evaluated in comparably large outdoor and indoor environments. We furthermore present experiments in which our technique is applied to build a map with a blimp.

Place recognition in 3D scans using a combination of bag of words and point feature based relative pose estimation

Place recognition, i.e., the ability to recognize previously seen parts of the environment, is one of the fundamental tasks in mobile robotics. The wide range of applications of place recognition includes localization (determine the initial pose), SLAM (detect loop closures), and change detection in dynamic environments. In the past, only relatively little work has been carried out to attack this problem using 3D range data and the majority of approaches focuses on detecting similar structures without estimating relative poses. In this paper, we present an algorithm based on 3D range data that is able to reliably detect previously seen parts of the environment and at the same time calculates an accurate transformation between the corresponding scan-pairs. Our system uses the estimated transformation to evaluate a candidate and in this way to more robustly reject false positives for place recognition. We present an extensive set of experiments using publicly available datasets in which we compare our system to other state-of-the-art approaches.

Mapping indoor environments based on human activity

We present a novel approach to build approximate maps of structured environments utilizing human motion and activity. Our approach uses data recorded with a data suit which is equipped with several IMUs to detect movements of a person and door opening and closing events. In our approach we interpret the movements as motion constraints and door handling events as landmark detections in a graph-based SLAM framework. As we cannot distinguish between individual doors, we employ a multi-hypothesis approach on top of the SLAM system to deal with the high data-association uncertainty. As a result, our approach is able to accurately and robustly recover the trajectory of the person. We additionally take advantage of the fact that people traverse free space and that doors separate rooms to recover the geometric structure of the environment after the graph optimization. We evaluate our approach in several experiments carried out with different users and in environments of different types.

Activity-Based Estimation of Human Trajectories

We present a novel approach to incrementally determine the trajectory of a person in 3-D based on its motions and activities in real time. In our algorithm, we estimate the motions and activities of the user given the data that are obtained from a motion capture suit equipped with several inertial measurement units. These activities include walking up and down staircases, as well as opening and closing doors. We interpret the first two types of activities as motion constraints and door-handling events as landmark detections in a graph-based simultaneous localization and mapping (SLAM) framework. Since we cannot distinguish between individual doors, we employ a multihypothesis tracking approach on top of the SLAM procedure to deal with the high data-association uncertainty. As a result, we are able to accurately and robustly recover the trajectory of the person. Additionally, we present an algorithm to build approximate geometrical and topological maps based on the estimated trajectory and detected activities. We evaluate our approach in practical experiments that are carried out with different subjects and in various environments.

Range-Based People Detection and Tracking for Socially Enabled Service Robots

With a growing number of robots deployed in populated environments, the ability to detect and track humans, recognize their activities, attributes and social relations are key components for future service robots. In this article we will consider fundamentals towards these goals and present several results using 2D range data.We first propose a learning method to detect people in sensory data based on a set of boosted features. The method largely outperforms the state of the art that typically relies on hand-tuned classifiers. Then, we present a person tracking approach based on the detection and fusion of leg tracks. To deal with the frequent occlusion and self-occlusion of legs, we extend a Multi-Hypothesis Tracking (MHT) approach by the ability to explicitly reason about and deal with adaptive occlusion probabilities. Finally, we address the problem of tracking groups of people, a first step towards the recognition of social relations. We further extend the MHT approach by a multiple model hypothesis stage able to reflect split/merge events in group formation processes. The proposed extension is mathematically elegant, runs in real-time and further allows to accurately estimate the number of people in each group. The article concludes with prospects and suggestions for future research.