A Proposal for Semantic Map Representation and Evaluation

Semantic mapping is the incremental process of “mapping” relevant information of the world (i.e., spatial information, temporal events, agents and actions) to a formal description supported by a reasoning engine. Current research focuses on learning the semantic of environments based on their spatial location, geometry and appearance. Many methods to tackle this problem have been proposed, but the lack of a uniform representation, as well as standard benchmarking suites, prevents their direct comparison. In this paper, we propose a standardization in the representation of semantic maps, by defining an easily extensible formalism to be used on top of metric maps of the environments. Based on this, we describe the procedure to build a dataset (based on real sensor data) for benchmarking semantic mapping techniques, also hypothesizing some possible evaluation metrics. Nevertheless, by providing a tool for the construction of a semantic map ground truth, we aim at the contribution of the scientific community in acquiring data for populating the dataset

Knowledge Representation for Robots through Human-Robot Interaction

The representation of the knowledge needed by a robot to perform complex tasks is restricted by the limitations of perception. One possible way of overcoming this situation and designing "knowledgeable" robots is to rely on the interaction with the user. We propose a multi-modal interaction framework that allows to effectively acquire knowledge about the environment where the robot operates. In particular, in this paper we present a rich representation framework that can be automatically built from the metric map annotated with the indications provided by the user. Such a representation, allows then the robot to ground complex referential expressions for motion commands and to devise topological navigation plans to achieve the target locations.Comment: Knowledge Representation and Reasoning in Robotics Workshop at ICLP 201

Interactive semantic mapping: Experimental evaluation

Robots that are launched in the consumer market need to provide more effective human robot interaction, and, in particular, spoken language interfaces. However, in order to support the execution of high level commands as they are specified in natural language, a semantic map is required. Such a map is a representation that enables the robot to ground the commands into the actual places and objects located in the environment. In this paper, we present the experimental evaluation of a system specifically designed to build semantically rich maps, through the interaction with the user. The results of the experiments not only provide the basis for a discussion of the features of the proposed approach, but also highlight the manifold issues that arise in the evaluation of semantic mapping

Conceptual spatial representations for indoor mobile robots

We present an approach for creating conceptual representations of human-made indoor environments using mobile robots. The concepts refer to spatial and functional properties of typical indoor environments. Following findings in cognitive psychology, our model is composed of layers representing maps at different levels of abstraction. The complete system is integrated in a mobile robot endowed with laser and vision sensors for place and object recognition. The system also incorporates a linguistic framework that actively supports the map acquisition process, and which is used for situated dialogue. Finally, we discuss the capabilities of the integrated system

Experiences on a motivational learning approach for robotics in undergraduate courses

This paper presents an educational experience carried out in robotics undergraduate courses from two different degrees: Computer Science and Industrial Engineering, having students with diverse capabilities and motivations. The experience compares two learning strategies for the practical lessons of such courses: one relies on code snippets in Matlab to cope with typical robotic problems like robot motion, localization, and mapping, while the second strategy opts for using the ROS framework for the development of algorithms facing a competitive challenge, e.g. exploration algorithms. The obtained students’ opinions were instructive, reporting, for example, that although they consider harder to master ROS when compared to Matlab, it might be more useful in their (robotic related) professional careers, which enhanced their disposition to study it. They also considered that the challenge-exercises, in addition to motivate them, helped to develop their skills as engineers to a greater extent than the skeleton-code based ones. These and other conclusions will be useful in posterior courses to boost the interest and motivation of the students.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Investigations On Human Perceptual Maps Using A Stereo-Vision Mobile Robot

Spatial cognition is a branch of cognitive psychology concerning the acquisition, organization, utilization, and revision of knowledge about spatial environments. A new computational theory of human spatial cognitive mapping has been proposed in the literature, and analyzed using a laser-based mobile robot. In contrast with the well-established SLAM (Simultaneous Localization and Mapping) approach that creates a precise and complete map of the environment, the proposed human perceptual map building procedure is more representative of spatial cognitive mapping in the human brain, whereby an imprecise and incomplete perceptual map of an environment can be created easily. The key steps in the methodology are capturing stereo-vision images of the environment, creating the tracked reference objects (TROs), tracking the number of remaining TROs, and expanding the map when the limiting points of the environment are reached. The main contribution of this research is on the use of computer vision techniques and computational mapping algorithms on a stereo-vision mobile robot for formulating the human perceptual map systematically, and evaluating the resulting human perceptual maps pertaining to both indoor and outdoor environments comprehensively. Validating the human perceptual maps using vision-based techniques is important for two reasons. Firstly, vision plays an important role in the development of human spatial cognition; secondly, computer vision systems are less expensive and information-rich in representing an environment. Specifically, computer vision techniques are first developed for analyzing the associated stereo images and retrieving the displacement information of a mobile robot, as well ascreating the necessary tracked reference objects. A number of computational mapping algorithms are then employed to build a human perceptual map of the environment in this research. Four real-world environments, namely two large indoor and two large outdoor environments, are empirically evaluated. The spatial geometry of the test environments vary, and the environments are subject to various natural effects including reflection and noise. The reflection and noise occurrin many parts of the images. Therefore, additional algorithms are developed in order to remove the reflection and noise. The removal of reflection and noise significantly reduces the number of TROs createdfor every immediate view. The outcomes indicate that the proposed computer vision techniques and computational mapping algorithms for human perceptual map building are robust and useful. They are able to create imprecise and incomplete human perceptual maps with good spatial representation of the overall environments. The map is imprecise and incomplete in the sense that it is not accurate in metric terms and has perceived surfaces missing. It is shown that both vision-based and the laser-based systems are able to computer a reasonably accurate spatial geometry of the tested environment