We propose a map merging algorithm that is capable of merging together heterogeneous maps independently built by different robots. Heterogeneous map merging is a crucially important problem for scenarios where multiple heterogeneous robots collaborate to provide situational awareness in urban search and rescue, patrolling, and explorations tasks, just to name a few. To remedy the lack of uniform representation between heterogeneous map models, we rely on the ubiquitous presence of WiFi signals in today's environments. Our solution consists of three steps. First, the overlap between the heterogeneous maps being merged is determined. Second, metric correspondences between overlapping parts are established. Third, the merging is improved by exploiting the structural properties inherent to graph-based maps. Our proposed system is validated using various occupancy grid and appearance-based maps built in real-world conditions, the results of which confirm its strengths. To the best of our knowledge, this is the first solution to the heterogeneous map merging problem.

Appearance based maps are emerging as an important class of spatial representations for mobile robots. In this paper we tackle the problem of merging together two or more appearance based maps independently built by robots operating in the same environment. Noticing the lack of well accepted metrics to measure the performance of map merging algorithms, we propose to use algebraic connectivity as a metric to assess the advantage gained by merging multiple maps. Next, based on this criterion, we propose an anytime algorithm aiming to quickly identify the more advantageous parts to merge. The system we proposed has been fully implemented and tested in indoor scenarios and shows that our algorithm achieves a convenient tradeoff between accuracy and speed.

We consider the problem of team-based robot mapping and localization using wireless signals broadcast from access points embedded in today's urban environments. We map and localize in an unknown environment, where the access points' locations are unspecified and for which training data is a priori unavailable. Our approach is based on an heterogeneous method combining robots with different sensor payloads. The algorithmic design assumes the ability of producing a map in real-time from a sensor-full robot that can quickly be shared by sensor-deprived robot team members. More specifically, we cast WiFi localization as classification and regression problems that we subsequently solve using machine learning techniques. In order to produce a robust system, we take advantage of the spatial and temporal information inherent in robot motion by running Monte Carlo Localization on top of our regression algorithm, greatly improving its effectiveness. A significant amount of experiments are performed and presented to prove the accuracy, effectiveness, and practicality of the algorithm.

We present a system that enables multiple heterogenous mobile robots to build and share an appearance based map appropriate for indoor navigation using exclusively monocular vision. Robots incrementally create online an appearance based model based on SIFT descriptors. The spatial model is enriched with additional information so that the map can be used for navigation also by robots different from those that built it. Once the map is available, navigation is performed using an approach based on epipolar geometry. The control mechanism builds upon the unicycle kinematic model, and assumes robots are equipped with a servoed camera. The validity of the proposed approach is substantiated both in simulation and on an heterogeneous multirobot system.

This paper presents an experimental validation of an online estimation algorithm we recently investigated theoretically. One of the peculiar characteristics of the approach we propose is the ability to perform an online estimation of the variance parameters that regulate the dynamics of the nonlinear dynamical model used. The approach exploits and extends classical iterated Kalman filtering equations by propagating an approximation of the marginal posterior of the unknown variances over time. The method has been previously used to model and solve a localization task for multiple robots equipped only with a sensor returning mutual distances. In this paper we present a first experimental validation of the algorithm that complements and confirms our initial promising theoretical findings. Our current implementation relies on a sensor returning distance estimates based on a simple image processing algorithm. Such sensor is inherently and intentionally noisy, and in this study we show that our technique is capable of appropriately estimating the variance describing the noise affecting this sensor. We conclude proving experimentally that the procedure we present ensures a performance comparable to similar algorithms that require significantly more a priori information.

The paper presents a genetic algorithm to find and optimize solutions for nonholonomic motion planning problems. Mainly focusing on mobile robots, the algorithm uses present randomized algorithms to come up with suboptimal paths and iteratively optimizes them according to a fitness function which includes domain specific knowledge. The major advantages of this method include being an any-time algorithm, and improving the quality of the solution throughout the evolutionary process. An extensive experimental analysis comparing our results with state of the art algorithms outline the effectiveness of the proposed methodology.