Autonomous mobile robots are becoming increasingly important in many industrial and domestic environments. Dealing with unforeseen situations is a difficult problem that must be tackled to achieve long-term robot autonomy. In vision-based localization and navigation methods, one of the major issues is the scene dynamics. The autonomous operation of the robot may become unreliable if the changes occurring in dynamic environments are not detected and managed. Moving chairs, opening and closing doors or windows, replacing objects and other changes make many conventional methods fail. To deal with these challenges, we present a novel method for change detection based on weighted local visual features. The core idea of the algorithm is to distinguish the valuable information in stable regions of the scene from the potentially misleading information in the regions that are changing. We evaluate the change detection algorithm in a visual localization framework based on feature matching by performing a series of long-term localization experiments in various real-world environments. The results show that the change detection method yields an improvement in the localization accuracy, compared to the baseline method without change detection. In addition, an experimental evaluation on a public long-term localization data set with more than 10000 images reveals that the proposed method outperforms two alternative localization methods on images recorded several months after the initial mapping.

The ability to search for objects is a precondition for various robotic tasks. In this paper, we address the problem of finding objects in partially known indoor environments. Using the knowledge of the floor plan and the mapped objects, we consider object-object and object-room co-occurrences as prior information for identifying promising locations where an unmapped object can be present. We propose an efficient search strategy that determines the best pose of the robot based on the analysis of the candidate locations. We optimize the probability of finding the target object and the distance travelled through a cost function.To evaluate our method, several experiments in simulated and real-world environments were performed. The results show that the robot successfully finds the target object in the environment while covering only a small portion of the search space. The real-world experiments with the TurtleBot 2 mobile robot validate the proposed approach and demonstrate that the method performs well also in real environments.

Relying on static representations of the environment limits the use of mapping methods in most real-world tasks. Real-world environments are dynamic and undergo changes that need to be handled through map adaptation. In this work, an object-based pose graph is proposed to solve the problem of mapping in indoor dynamic environments with mobile robots. In contrast to state-of-The art methods where binary classifications between movable and static objects are used, we propose a new method to capture the probability of different objects over time. Object probability represents how likely it is to find a specific object in its previous location and it gives a quantification of how movable specific objects are. In addition, grouping object probabilities according to object class allows us to evaluate the movability of different object classes. We validate our object-based pose graph in real-world dynamic environments. Results in mapping and map adaptation with a real robot show efficient map maintenance through several mapping sessions and results in object classification according to movability show an improvement compared to binary classification.

Autonomous mobile robots are becoming increasingly important in many industrial and domestic environments. Dealing with unforeseen situations is a difficult problem that must be tackled in order to move closer to the ultimate goal of life-long autonomy. In computer vision-based methods employed on mobile robots, such as localization or navigation, one of the major issues is the dynamics of the scenes. The autonomous operation of the robot may become unreliable if the changes that are common in dynamic environments are not detected and managed. Moving chairs, opening and closing doors or windows, replacing objects on the desks and other changes make many conventional methods fail. To deal with that, we present a novel method for change detection based on the similarity of local visual features. The core idea of the algorithm is to distinguish important stable regions of the scene from the regions that are changing. To evaluate the change detection algorithm, we have designed a simple visual localization framework based on feature matching and we have performed a series of real-world localization experiments. The results have shown that the change detection method substantially improves the accuracy of the robot localization, compared to using the baseline localization method without change detection.