Christof Schroeter

ShopBot: Progress in developing an interactive mobile shopping assistant for everyday use

The paper describes progress achieved in our long-term research project ShopBot, which aims at the development of an intelligent and interactive mobile shopping assistant for everyday use in shopping centers or home improvement stores. It is focusing on recent progress concerning two important methodological aspects: (i) the on-line building of maps of the operation area by means of advanced Rao-Blackwellized SLAM approaches using both sonar-based gridmaps as well as vision-based graph maps as representations, and (ii) a probabilistic approach to multi-modal user detection and tracking during the guidance tour. Experimental results of both the map building characteristics and the person tracking behavior achieved in an ordinary home improvement store demonstrate the reliability of both approaches. Moreover, we present first very encouraging results of long-term field trials which have been executed with three robotic shopping assistants in another home improvement store in Bavaria since March 2008. In this field test, the robots could demonstrate their suitability for this challenging real-world application, as well as the necessary user acceptance.

Omnivision-based probabilistic self-localization for a mobile shopping assistant continued

The basic idea of our omniview-based MCL approach and preliminary experimental results were presented in our previous paper [Proc. IROS 2002, pp. 256-262]. In continuing, this paper describes a number of methodical and technical improvements addressing challenges arising from the characteristics of our real-world application, the vision-based self-localization of a mobile robot that acts as a shopping assistant in the maze-like environment of a home store. To cope with highly variable illumination conditions, we present a reference-based correction approach that realizes a robust, automatic luminance stabilization and color adaptation already at the level of image formation. To deal with severe occlusions or disturbances of the omnidirectional image caused by, e.g. people standing near the robot or local illumination artifacts, we introduce a novel selective observation comparison method as prerequisite for a robust particle filter update. Further studies investigate the impact of the utilized observation model on the localization accuracy. The results of a series of localization experiments carried out in the home store confirm the robustness and superiority of our advanced, real-time approach.

Robot companion for domestic health assistance: Implementation, test and case study under everyday conditions in private apartments

This paper presents the implementation and evaluation results of the German research project SERROGA (2012 till mid 2015), which aimed at developing a robot companion for domestic health assistance for older people that helps keeping them physically and mentally fit to remain living independently in their own homes for as long as possible. The paper gives an overview of the developed companion robot, its system architecture, and essential skills, behaviors, and services required for a robotic health assistant. Moreover, it presents a new approach allowing a quantitative description and assessment of the navigation complexity of apartments to make them objectively comparable for function tests under real-life conditions. Based on this approach, the results of function tests executed in 12 apartments of project staff and seniors are described. Furthermore, the paper presents findings of a case study conducted with nine seniors (aged 68-92) in their own homes, investigating both instrumental and social-emotional functions of a robotic health assistant. The robot accompanied the seniors in their homes for up to three days assisting with tasks of their daily schedule and health care, without any supervising person being present on-site. Results revealed that the seniors appreciated the robots health-related instrumental functions and even built emotional bonds with it.

A sensor-independent approach to RBPF SLAM - Map Match SLAM applied to visual mapping

In this paper, we present the application of our generic, sensor-independent Map Match SLAM framework to visual mapping. In our previous work , we have introduced the map match SLAM approach for mapping with sonar range readings: Extending the grid-based Rao-Blackwellized particle filter SLAM approach, in Map Match SLAM, a local map is maintained by each particle in addition to the global map. The local map is used to represent the most recent observations, and weighting of the particles is done based on the compliance of the local and the global map. In this paper, we show how RBPF SLAM can also be applied for mapping and path reconstruction with a stereo camera or a single monocular camera, respectively. By mapping with completely different sensors such as sonar, stereo, or monocular cameras, we prove the wide range applicability of RBPF SLAM and our map match SLAM computational framework.

Autonomous robot cameraman - Observation pose optimization for a mobile service robot in indoor living space

This paper presents a model based system for a mobile robot to find an optimal pose for the observation of a person in indoor living environments. We define the observation pose as a combination of the camera position and view direction as well as further parameters like the aperture angle. The optimal placement of a camera is not trivial because of the high dynamic range of the scenes near windows or other bright light sources, which often results in poor image quality due to glare or hard shadows. The proposed method tries to minimize these negative effects by determining an optimal camera pose based on two major models: A spatial free space model and a representation of the lighting. In particular, a task-dependent optimization takes into account the intended purpose of the camera images, e.g. different inputs are needed for video communication with other people or for an image-processing based passive observation of the persons activities. To prove the validity of our approach, we present first experimental results comparing the chosen observation pose and resulting image with and without respect to lighting in different observation tasks.

People detection and distinction of their walking aids in 2D laser range data based on generic distance-invariant features

People detection in 2D laser range data is a popular cue for person tracking in mobile robotics. Many approaches are designed to detect pairs of legs. These approaches perform well in many public environments. However, we are working on an assistance robot for stroke patients in a rehabilitation center, where most of the people need walking aids. These tools occlude or touch the legs of the patients. Thereby, approaches based on pure leg detection fail. The essential contribution of this paper are generic distance-invariant range scan features for people detection in 2D laser range data and the distinction of their walking aids. With these features we trained classifiers for detecting people without walking aids (or with crutches), people with walkers, and people in wheelchairs. Using this approach for people detection, we achieve an F1 score of 0.99 for people with and without walking aids, and 86% of detections are classified correctly regarding their walking aid. For comparison, using state-of-the-art features of Arras et al. on the same data results in an F1 score of 0.86 and 57% correct discrimination of walking aids. The proposed detection algorithm takes around 2.5% of the resources of a 2.8 GHz CPU core to process 270° laser range data at an update rate of 10 Hz.

Extraction of Orientation from Floor Structure for Odometry Correction in Mobile Robotics

We are presenting a method for correcting odometry readings of a robot for increased accuracy of position estimation. Our method uses a simple pragmatic approach and exploits the distinct structure of the floor in our experimental area. By continually extracting orientation information from the floor view, we are able to correct the heading component of odometry, thereby eliminating the major source for position errors. Compared to other approaches the solution is computationally inexpensive. Our experiments show that by employing our correction method we are able to significantly increase position accuracy and consistently map paths up to several hundred meters.

Robust map building for an autonomous robot using low-cost sensors

The paper describes an approach for building a map of an indoor environment with a mobile robot, using a combination of odometry and sonar range sensors. Aiming for real-time large scale mapping on low-cost platforms with limited sensory and computational equipment, we discard high-complexity techniques like probabilistic SLAM. Instead, the algorithms presented here involve odometry correction and automatic pose recalibration, enabling us to build a coherent map that can be used for navigation and self-localization. Experimental results from different environments prove the efficiency of our approach.

“Go Ahead, Please”: Recognition and Resolution of Conflict Situations in Narrow Passages for Polite Mobile Robot Navigation

For a mobile assistive robot operating in a human-populated environment, a polite navigation is an important requirement for the social acceptance. When operating in a confined environment, narrow passages can lead to deadlock situations with persons. In our approach we distinguish two types of deadlock situations at narrow passages, in which the robot lets the conflicting person pass, and either waits in a non-disturbing waiting position, or forms a queue with that person. Forthcoming deadlock situations are captured by a set of qualitative features. As part of these features, we detect narrow passages with a raycasting approach and predict the movement of persons. In contrast to numerical features, the qualitative description forms a more compact human-understandable space allowing to employ a rule-based decision tree to classify the considered situation types. To determine a non-disturbing waiting position, a multi-criteria optimization approach is used together with the Particle Swarm Optimization as solver. In field tests, we evaluated our approach for deadlock recognition in a hospital environment with narrow corridors.

Monocular Obstacle Detection for Real-World Environments

In this paper, we present a feature based approach for monocular scene reconstruction based on extended Kaiman filters (EKF). Our method processes a sequence of images taken by a single camera mounted in front of a mobile robot. Using various techniques we are able to produce a precise reconstruction that is almost free from outliers and therefore can be used for reliable obstacle detection and avoidance. In real-world field tests we show that the presented approach is able to detect obstacles that can not be seen by other sensors, such as laser range finders. Furthermore, we show that visual obstacle detection combined with a laser range finder can increase the detection rate of obstacles considerably, allowing the autonomous use of mobile robots in complex public and home environments.