A predictor for operator input for time-delayed teleoperation

In this paper we describe a method for bridging internet time delays in a free motion type teleoperation scenario in an unmodeled remote environment with video feedback. The method proposed uses minimum jerk motion models to predict the input from the user a time into the future that is equivalent to the round-trip communication delay. The predictions are then used to control a remote robot. Thus, the operator can in effect observe the resulting motion of the remote robot with virtually no time-delay, even in the presence of a delay on the physical communications channel. We present results from a visually guided teleoperated line tracing experiment with 100 ms round-trip delays, where we show that the proposed method makes a significant performance improvement for teleoperation with delays corresponding to intercontinental distances.     

Topological spatial relations for active visual search

If robots are to assume their long anticipated place by humanity’s side and be of help to us in our partially structured environments, we believe that adopting human-like cognitive patterns will be valuable. Such environments are the products of human preferences, activity and thought; they are imbued with semantic meaning. In this paper we investigate qualitative spatial relations with the aim of both perceiving those semantics, and of using semantics to perceive. More specifically, in this paper we introduce general perceptual measures for two common topological spatial relations, “on” and “in”, that allow a robot to evaluate object configurations, possible or actual, in terms of those relations. We also show how these spatial relations can be used as a way of guiding visual object search. We do this by providing a principled approach for indirect search in which the robot can make use of known or assumed spatial relations between objects, significantly increasing the efficiency of search by first looking for an intermediate object that is easier to find. We explain our design, implementation and experimental setup and provide extensive experimental results to back up our thesis.     

The M-Space Feature Representation for SLAM

In this paper, a new feature representation for simultaneous localization and mapping (SLAM) is discussed. The representation addresses feature symmetries and constraints explicitly to make the basic model numerically robust. In previous SLAM work, complete initialization of features is typically performed prior to introduction of a new feature into the map. This results in delayed use of new data. To allow early use of sensory data, the new feature representation addresses the use of features that initially have been partially observed. This is achieved by explicitly modelling the subspace of a feature that has been observed. In addition to accounting for the special properties of each feature type, the commonalities can be exploited in the new representation to create a feature framework that allows for interchanging of SLAM algorithms, sensor and features. Experimental results are presented using a low-cost Web-cam, a laser range scanner, and combinations thereof.     

A realistic benchmark for visual indoor place recognition

An important competence for a mobile robot system is the ability to localize and perform context interpretation. This is required to perform basic navigation and to facilitate local specific services. Recent advances in vision have made this modality a viable alternative to the traditional range sensors, and visual place recognition algorithms emerged as a useful and widely applied tool for obtaining information about robot’s position. Several place recognition methods have been proposed using vision alone or combined with sonar and/or laser. This research calls for standard benchmark datasets for development, evaluation and comparison of solutions. To this end, this paper presents two carefully designed and annotated image databases augmented with an experimental procedure and extensive baseline evaluation. The databases were gathered in an uncontrolled indoor office environment using two mobile robots and a standard camera. The acquisition spanned across a time range of several months and different illumination and weather conditions. Thus, the databases are very well suited for evaluating the robustness of algorithms with respect to a broad range of variations, often occurring in real-world settings. We thoroughly assessed the databases with a purely appearance-based place recognition method based on support vector machines and two types of rich visual features (global and local).