This paper presents a graph-based visual simultaneous localization and mapping (SLAM) system using straight lines as features. Compared with point features, lines provide far richer information about the structure of the environment and make it possible to infer spatial semantics from the map. Using a stereo rig as the sole sensor, our proposed system utilizes many advanced techniques, such as motion estimation, pose optimization, and bundle adjustment. We use two different representations to parameterize 3-D lines in this paper: Plücker line coordinates for efficient initialization of newly observed line features and projection of 3-D lines, and orthonormal representation for graph optimization. The proposed system is tested with indoor and outdoor sequences, and it exhibits better reconstruction performance against a point-based SLAM system in line-rich environments.

In this paper, we propose a visual place recognition algorithm which uses only straight line features in challenging outdoor environments. Compared to point features used in most existing place recognition methods, line features are easily found in man-made environments and more robust to environmental changes such as illumination, viewing direction, or occlusion because they are more likely to be extracted from structures. Candidate matches are found using a vocabulary tree and their geometric consistency is verified by a motion estimation algorithm using line segments. The proposed algorithm operates in real-time, and it is tested with a challenging real-world dataset with more than 10,000 database images acquired in urban driving scenarios.

Most visual simultaneous localization and mapping systems use point features as their landmarks and adopt point-based feature descriptors to recognize them. Compared to point landmarks, however, lines have strength in conveying the structural information of the environment. Despite the benefit, they have not been widely used because lines are more difficult in detecting, tracking, and recognizing, and this delayed the use of lines as landmarks. In this paper, we propose a place recognition algorithm using straight line features, which enables reliable loop closure detections in large complex environments under significant illumination changes. A vocabulary tree trained with mean standard-deviation line descriptor is used in finding the candidate matches between keyframes, and a Bayesian filtering framework enables reliable keyframe matching for large-scale loop closures. The proposed algorithm is compared with state-of-the-art point-based methods using scale-invariant feature transform or speeded up robust features. The experimental results show that the proposed method outperforms the others in challenging indoor environments.

In this paper, we present a vanishing point-based two-step loop closure method in a line-based monocular simultaneous localization and mapping (SLAM) system. Vanishing points can provide absolute directional landmarks for mobile robots. This guiding ability is important in that the observation of the vanishing points is invariant with respect to the robot pose. In our system, loop closure is performed in two steps: first, the accumulated heading error is reduced using an observation of previously registered vanishing points, and second, the observation of known floor lines allows for further pose correction. In this paper, we apply this method to solve the loop closure problem for a line-based SLAM within a corridor environment where the vanishing points are usually easily detectable. Experimental results show that our method is very efficient in a structured indoor environment.

In this paper, we present a monocular SLAM method which uses vertical and the floor line features as sensory input. A line-based partial 3D map was built, in which many structural properties of the environment can be included. The vertical line and the floor line can be represented as simple 2D objects in the floor plane. These two heterogeneous types of line features were developed as independent SLAM features and combined to represent the environment in a more complete fashion. Although the vertical and floor line use different parameterization and initialization methods, their measurement models are integrated into a unified EKF framework. Experimental results show that this method is practical in a structured indoor environment.

In this paper, we propose a novel monocular SLAM method in corridor environment which employs Segments-on-Floor (SoF) as feature data. Given that the height of the camera and the angle between the camera and the floor are known, an image of the SoF can be efficiently distinguished from the other space-lines by a simple data-association method, deriving the line correspondence from a simplified homography matrix of two sequentially gathered images. Furthermore, use of SoF simplifies the analysis of the geometrical property of the camera projection matrix. Therefore, we can reconstruct SoF by using a one-step inverse projection. Once SoF is calculated from visual data processing, they are then used in a normal SLAM process as feature data. We employ a simple particle filter in our corridor SLAM. Experimental results show that it is sufficient for mapping a moderately sized building environment.

In the task of robot localization, Bayes filters use two processes: the prediction step and the measurement-update step. Briefly, the state transition model is responsible for prediction, and the sensor model is responsible for measurement updates. This paper presents a new approach to the sensor model, called the predictive sensor model, which utilizes a prediction mechanism to improve the efficiency of measurement updates in Bayes filters. By adding sensorial anticipation, we extend the original Bayes filter to an anticipatory Bayes filter. We also propose an entropy-based place-segmentation method for automatic segmentation of sequentially collected vision-sensor data. Our place segmentation technique is most useful for node clustering in the process of constructing topological maps. Our work was verified by experiments using observed data.

Prediction is a very important element of human intelligence and plays a major role in human behavior, perception, and learning. This paper presents the development of a mathematical model of the prediction mechanism in the context of a Bayes filter, which is the predominant schema used for integrating temporal data in the field of robot mapping and localization problems. We propose a generalized anticipatory Bayes filter that uses revised sensor values obtained from the prediction process at the measurement-update step to enhance the performance of the sensor model. The development of a generalized anticipatory Bayes filter is not only an extension of the original Bayes filter, but also a mathematical model of the human prediction mechanism of sensory processing. This work was verified by experiments using observed data.