This paper is centered around landmark detection, tracking and matching for visual SLAM (Simultaneous Localization And Mapping) using a monocular vision system with active gaze control. We present a system specialized in creating and maintaining a sparse set of landmarks based on a biologically motivated feature selection strategy. A visual attention system detects salient features which are highly discriminative, ideal candidates for visual landmarks which are easy to redetect. Features are tracked over several frames to determine stable landmarks and to estimate their 3D position in the environment. Matching of current landmarks to database entries enables loop closing. Active gaze control allows us to overcome some of the limitations of using a monocular vision system with a relatively small field of view. It supports (i) the tracking of landmarks which enable a better pose estimation, (ii) the exploration of regions without landmarks to obtain a better distribution of landmarks in the environment, and (iii) the active redetection of landmarks to enable loop closing in situations in which a fixed camera fails to close the loop. Several real-world experiments show that accurate pose estimation is obtained with the presented system and that active camera control outperforms the passive approach.

Abstract. Visual attention regions are useful for many applications in the field of computer vision and robotics. Here, we introduce an application to simultaneous robot localization and mapping. A biologically motivated attention system finds regions of interest which serve as visual landmarks for the robot. The regions are tracked and matched over consecutive frames to build stable landmarks and to estimate the 3D position of the landmarks in the environment. Matching of current landmarks to database entries enables loop closing and global localization. Additionally, the system is equipped with an active camera control, which supports the system with a tracking, a re-detection, and an exploration behaviour. We present experiments which show the applicability of the system in a real-world scenario. A comparison between the system operating in active and in passive mode shows the advantage of active camera control: we achieve a better distribution of landmarks as well as a faster and more reliable loop closing. 1

In this paper, we propose a new, hierarchical approach to landmark selection for simultaneous robot localization and mapping based on visual sensors: a biologically motivated attention system finds salient regions of interest (ROIs) in images, and within these regions, Harris corners are detected. This combines the advantages of the ROIs (reducing complexity, enabling good redetactability of regions) with the advantages of the Harris corners (high stability). Reducing complexity is important to meet real-time requirements and stability of features is essential to compute the depth of landmarks from structure from motion with a small baseline. We show that the number of landmarks is highly reduced compared to all Harris corners while maintaining the stability of features for the mapping task. 1.

In implementing a vision localization system, a crucial issue to consider is how to efficiently store and recall the necessary information so that the robot is not only able to accurately localize itself, but does so in a timely manner. In the presented system, we discuss a strategy to minimize the amount of stored data by analyzing the strengths and weaknesses of several cooperating recognition modules, and by using them through a prioritization scheme, which orders the data entries from the most likely to match to the least. We validate the system is a series of experiments at three large scale outdoor

email In this paper, we propose a new approach for landmark selection for simultaneous robot localization and mapping based on visual sensors (VSLAM): a biologically motivated attention system finds regions of interest with high information content, and at these regions, Harris corners are detected. This combines the advantages of the ROIs (reducing complexity, enabling good redetactability of regions) with the advantages of the Harris corners (high stability). Reducing complexity is important to meet real-time requirements and stability of features is essential to compute the depth of landmarks from structure from motion with a small baseline. We show that the number of landmarks is highly reduced compared to all Harris corners while keeping the stability of the features. 1.