Ioannis Kostavelis

Semantic mapping for mobile robotics tasks

The evolution of contemporary mobile robotics has given thrust to a series of additional conjunct technologies. Of such is the semantic mapping, which provides an abstraction of space and a means for human-robot communication. The recent introduction and evolution of semantic mapping motivated this survey, in which an explicit analysis of the existing methods is sought. The several algorithms are categorized according to their primary characteristics, namely scalability, inference model, temporal coherence and topological map usage. The applications involving semantic maps are also outlined in the work at hand, emphasizing on human interaction, knowledge representation and planning. The existence of publicly available validation datasets and benchmarking, suitable for the evaluation of semantic mapping techniques is also discussed in detail. Last, an attempt to address open issues and questions is also made. Two level navigation.Cognitive navigation.Spatial semantics.

Learning spatially semantic representations for cognitive robot navigation

Contemporary mobile robots should exhibit enhanced capacities, which allow them self-localization and semantic interpretation as they move into an unexplored environment. The coexistence of accurate SLAM and place recognition can provide a descriptive and adaptable navigation model. In this paper such a two-layer navigation scheme is introduced suitable for indoor environments. The low layer comprises a 3D SLAM system based solely on an RGB-D sensor, whilst the high one employs a novel content-based representation algorithm, suitable for spatial abstraction. In course of robots locomotion, salient visual features are detected and they shape a bag-of-features problem, quantized by a Neural Gas to code the spatial information for each scene. The learning procedure is performed by an SVM classifier able to accurately recognize multiple dissimilar places. The two layers mutually interact with a semantically annotated topological graph augmenting the cognition attributes of the integrated system. The proposed framework is assessed on several datasets, exhibiting remarkable accuracy. Moreover, the appearance based algorithm produces semantic inferences suitable for labeling unexplored environments.

Stereovision-Based Algorithm for Obstacle Avoidance

This work presents a vision-based obstacle avoidance algorithm for autonomous mobile robots. It provides an efficient solution that uses a minimum of sensors and avoids, as much as possible, computationally complex processes. The only sensor required is a stereo camera. The proposed algorithm consists of two building blocks. The first one is a stereo algorithm, able to provide reliable depth maps of the scenery in frame rates suitable for a robot to move autonomously. The second building block is a decision making algorithm that analyzes the depth maps and deduces the most appropriate direction for the robot to avoid any existing obstacles. The proposed methodology has been tested on sequences of self-captured outdoor images and its results have been evaluated. The performance of the algorithm is presented and discussed.

Robot Guided Crowd Evacuation

The congregation of crowd undoubtedly constitutes an important risk factor, which may endanger the safety of the gathered people. The solution reported against this significant threat to citizens safety is to consider careful planning and measures. Thereupon, in this paper, we address the crowd evacuation problem by suggesting an innovative technological solution, namely, the use of mobile robot agents. The contribution of the proposed evacuation system is twofold: (i) it proposes an accurate Cellular Automaton simulation model capable of assessing the human behavior during emergency situations and (ii) it takes advantage of the simulation output to provide sufficient information to the mobile robotic guide, which in turn approaches and redirects a group of people towards a less congestive exit at a time. A custom-made mobile robotic platform was accordingly designed and developed. Last, the performance of the proposed robot guided evacuation model has been examined in real-world scenarios exhibiting significant performance improvement during the crucial first response time window.

Robot navigation via spatial and temporal coherent semantic maps

The ability of mobile robots to sense, interpret and map their environments in human terms is decisive for their applicability to everyday activities hereafter. Bearing this view in mind, we present here, for the first time, an integrated framework that aims: (i) to introduce a semantic mapping method and (ii) to use this semantic map, as a means to provide a hierarchical navigation solution. The semantic map is formed in a bottom-up fashion, along the robots course, relying on the conceptual space quantization, the time proximity and the spatial coherence integrated into the labeled sparse topological map. A novel time-evolving augmented navigation graph determines the semantic topology of the explored environment and the connectivity among the recognized places expressed by the inter-place transition probability. The robot navigation part is addressed through an interface that facilitates human robot interaction. High level orders are passed to the robots and unfolded recursively, in a top-down fashion, into local navigation data. The performance of the proposed framework was evaluated on long range real world data and exhibited remarkable results. HighlightsLabeled sparse topological map.Augmented navigation graph.Semantic mapping.Robot navigation.

SPARTAN: Developing a Vision System for Future Autonomous Space Exploration Robots

Mars exploration is expected to remain a focus of the scientific community in the years to come. A Mars rover should be highly autonomous because communication between the rover and the terrestrial operation center is difficult, and because the vehicle should spend as much of its traverse time as possible moving. Autonomous behavior of the rover implies that the vision system provides both a wide view to enable navigation and three-dimensional (3D) reconstruction, and at the same time a close-up view ensuring safety and providing reliable odometry data. The European Space Agency funded project “SPAring Robotics Technologies for Autonomous Navigation” (SPARTAN) aimed to develop an efficient vision system to cover all such aspects of autonomous exploratory rovers. This paper presents the development of such a system, starting from the requirements up to the testing of the working prototype. The vision system was designed with the intention of being efficient, low-cost, and accurate and to be implemented using custom-designed vectorial processing by means of field programmable gate arrays (FPGAs). A prototype of the complete vision system was developed, mounted on a basic mobile robot platform, and tested. The results on both real-world Mars-like and long-range simulated data are presented in terms of 3D reconstruction and visual odometry accuracy, as well as execution speed. The developed system is found to fulfill the set requirements.

On the optimization of Hierarchical Temporal Memory

In this paper an optimized classification method for object recognition is presented. The proposed method is based on the Hierarchical Temporal Memory (HTM), which stems from the memory prediction theory of the human brain. As in HTM, this method comprises a tree structure of connected computational nodes, whilst utilizing different rules to memorize objects appearing in various orientations. These rules involve both the spatial and the temporal module. As HTM is inspired from brain activity, its input should also comply with the human vision system. Thus, for the representation of the input images the logpolar was given preference to the Cartesian one. As compared to the original HTM method, experimental results exhibit performance enhancements with this approach, in recognition and categorization applications. Results obtained prove that the proposed method is more accurate and faster in training, whilst retaining the network robustness in multiple orientation variations.

Robot navigation in large-scale social maps

Integrated robot framework t for navigation in a habituated environment is proposed.The robots behavior obeys the rules of proxemics theory.Exploits the performed actions of humans to infer about its trajectory. As robots tend to establish their presence in every day human environments the necessity for them to attain socially acceptable behavior is a condition sine qua non. Consequently, robots need to learn and react appropriately, should they be able to share the same space with people and to reconcile their operation to mans activity. This work proposes an integrated robot framework that allows navigation in a human populated environment. This is the first work that employs the performed actions of individuals so as to re-plan and design a collision-free and at the same time a socially acceptable trajectory. Expandability is another feature of the suggested mapping module since it is capable of incorporating an unconstrained number of actions and subsequently responses, according to the needs of the task in hand and the environment in which the robot operates. Moreover, the paper addresses the integration of the proposed mapping module with the rest of the robot framework in order to operate in a seamless fashion. The generic design of this architecture allows the replacement of modules with other similar ones, thus providing adaptability with respect to the environment and so on. The method utilizes off-line constructed 3D metric maps organized in terms of a topological graph. During its perambulation the robot is ample to detect humans while it exploits deep learning strategies to recognize their activities. The memorized actions are seamlessly associated with specific rules -deriving from the proxemics theory- and are organized in an efficient manner to be recalled during robots navigation. Moreover, the paper exhibits the differences of the robot navigation in inhabited and uninhabited environments and demonstrates the alteration of the robots trajectory with respect to the recognized actions and poses of the individuals. The system has been evaluated on a robot able to acquire RGB-D data in domestic environments. The human detection and the action recognition modules exhibited remarkable performance, the human detection one was flawless about its decision while the action recognition one confused actions regarding the number of individuals that participate in them. Last, the robot navigation component was proved capable of extracting safe trajectories in human populated environments.

Visual Odometry for autonomous robot navigation through efficient outlier rejection

The ability of autonomous robots to precisely compute their spatial coordinates constitutes an important attribute. In this regard, Visual Odometry (VO) becomes a most appropriate tool, in estimating the full pose of a camera, placed onboard a robot by analyzing a sequence of images. The paper at hand proposes an accurate computationally-efficient VO algorithm relying exclusively on stereo vision. A non-iterative outlier detection technique capable of efficiently discarding outliers of matched features is suggested. The developed technique is combined with an incremental motion estimation approach to estimate the robots trajectory. The accuracy of the proposed system has been evaluated both on simulated data and using a real robotic platform. Experimental results from rough terrain routes show remarkable accuracy with positioning errors as low as 1.1%.

Object recognition using saliency maps and HTM learning

In this paper a pattern classification and object recognition approach based on bio-inspired techniques is presented. It exploits the Hierarchical Temporal Memory (HTM) topology, which imitates human neocortex for recognition and categorization tasks. The HTM comprises a hierarchical tree structure that exploits enhanced spatiotemporal modules to memorize objects appearing in various orientations. In accordance with HTMs biological inspiration, human vision mechanisms can be used to preprocess the input images. Therefore, the input images undergo a saliency computation step, revealing the plausible information of the scene, where a human might fixate. The adoption of the saliency detection module releases the HTM network from memorizing redundant information and augments the classification accuracy. The efficiency of the proposed framework has been experimentally evaluated in the ETH-80 dataset, and the classification accuracy has been found to be greater than other HTM systems.