Abstract — Today, lightweight SLAM algorithms are needed in many embedded robotic systems. In this paper the Orthogonal SLAM (OrthoSLAM) algorithm is presented and empirically validated. The algorithm has constant time complexity in the state estimation and is capable to run real-time. The main contribution resides in the idea of reducing the complexity by means of an assumption on the environment. This is done by mapping only lines that are parallel or perpendicular to each other which represent the main structure of most indoor environments. The combination of this assumption with a Kalman Filter and a Relative Map approach is able to map our laboratory hallway with the size of 80m × 50m and a trajectory of more than 500m. The precision of the resulting map is similar to the measurements done by hand which are used as the ground-truth.

Abstract — The future of robots, as our companions is dependent on their ability to understand, interpret and represent the environment in a human compatible manner. Towards this aim of making robots more spatially cognizant, the presented work is part of an attempt to create a hierarchical probabilistic concept-oriented representation of space, based on objects. Specifically, this work details efforts taken towards learning and generating concepts from the perceived objects and attempts to classify places using the concepts gleaned. The approach is based on learning from exemplars, clustering and the use of Bayesian network classifiers. Experiments on conceptualization and place classification are reported. Thus, the theme of the work is- conceptualization and classification for representation and spatial cognition. I.

Abstract: One of the ultimate goals in robotics is to make high-DOF robots work autonomously in unknown changing environments. However, motion planning in completely unknown environments is largely an open problem and poses many challenges. One challenge is that in such an environment, the configuration-time space (CT-space) of a robot is not known beforehand. This paper describes how guaranteed collision-free regions in the unknown CT-space can be discovered progressively via sensing in real time based on the concept dynamic envelope, which is not conservative, i.e., does not assume worst-case scenarios, and is robust to uncertainties in obstacle behaviors. The introduced method can be used in general by real-time motion planners for high-DOF robots to discover the existence of guaranteed collisionfree future motions efficiently. The utility is further confirmed both in simulation and in real-world testing involving a 5-DOF robot manipulator. 1

Soon, in many service robotic applications, a realtime localization and 3D-mapping capability will be necessary for autonomous navigation. Toward a light and practical SLAM algorithm for indoor scenarios, we propose a fast SLAM algorithm which benefits from sensor geometry for feature extraction and enhance the mapping process using dominant orthogonality in the engineered structures of man-made environments. Range images obtained using a nodding SICK are segmented into planar patches with polygonal boundaries in linear time. Right corner features are constructed based on the recognized orthogonal planes and used for robot localization. In addition to these corners, the map also contains planar patches with inner and outer boundaries for 3D modeling and recognition of the major building structures. Experiments using a mobile robot in our laboratory hallway prove the effectiveness of our approach. Results of the algorithm are compared with hand-measured ground truth.

Abstract — We describe an efficient, low-cost, low-overhead system for robot localization in complex visual environments. Our system augments wheel odometry with visual orientation tracking to yield localization accuracy comparable with “pure” visual odometry at a fraction of the cost. Such a system is well-suited to consumer-level robots, small form-factor robots, extraterrestrial rovers, and other platforms with limited computational resources. Our system also benefits high-end multiprocessor robots by leaving ample processor time on all cameracomputer pairs to perform other critical visual tasks, such as obstacle detection. Experimental results are shown for outdoor, off-road loops on the order of 200 meters. Comparisons are made with corresponding results from a state-of-the-art pure visual odometer. I.

Exploration is one of the main research topics in the field of mobile robotics. Multiple robots have been applied for different exploration algorithms In most of the these algorithms the robots exchange data via wireless communication assuming perfect data transmission in the whole environment. Unfortunately, this assumption does not always hold, as e.g. the range of wireless communication is limited. In this thesis Communicative Exploration is presented, which is based on Frontier-Based Exploration. For communicative exploration a group of robots is exploring unknown environments and maintain communication throughout the whole exploration process. The robots form a mobile ad-hoc network that is fully connected for the entire exploration. The motion of the robots is controlled via a heuristic utility function which assigns values to a population of configurations. The configuration with the highest utility value defines the motions of the robots. Different versions of communicative exploration

Abstract — Simple, fast and lightweight SLAM algorithms are necessary in many embedded robotic systems which soon will be used in houses and offices in order to do various service tasks. In this paper the Orthogonal SLAM algorithm is presented as an answer to this need. In continuation of our previous work, the algorithm is extended to generate 3D maps and empirically validated by mapping the long corridor of our lab with the accuracy comparable with hand measured ground truth. The main contribution resides in the idea of reducing the complexity by using orthogonality constraint in indoor environments. This is done by mapping only planes that are parallel or perpendicular to each other which represent the main structure of most indoor environments. Having this assumption, we use an inclined sensor setup (fixed 2D SICK laser range finders) to generate 3D orthogonal maps. The algorithm is extremely fast since in each step it just processes one line of laser measurements. I.

Abstract — The future of robots, as our companions is dependent on their ability to understand, interpret and represent the environment in a human compatible manner. Towards this aim, the presented work is part of an attempt to create a hierarchical probabilistic concept-oriented representation of space, based on objects. Specifically, this work details efforts taken towards learning and generating concepts and attempts to classify places using the concepts gleaned. Inference is based on the number of occurrences of various objects. The approach is based on learning from exemplars, clustering and the use of Bayesian network classifiers. Such a conceptualization and the representation that results thereof would be useful for enabling robots to be more cognizant of their surroundings and yet, compatible to us. Experiments on conceptualization and place classification are reported. Thus, the theme of the work is- conceptualization and classification for representation and spatial cognition. I.

In this paper, we demonstrate how simultaneous localization and mapping techniques (SLAM) from robotics can be used in wearable computing to automatically create floor maps from sensor data recorded by a pedestrian. We give preliminary results based on different self-localization and motion models. 1.