Abstract—We present a real-time algorithm which can recover the 3D trajectory of a monocular camera, moving rapidly through a previously unknown scene. Our system, which we dub MonoSLAM, is the first successful application of the SLAM methodology from mobile robotics to the “pure vision ” domain of a single uncontrolled camera, achieving real time but drift-free performance inaccessible to Structure from Motion approaches. The core of the approach is the online creation of a sparse but persistent map of natural landmarks within a probabilistic framework. Our key novel contributions include an active approach to mapping and measurement, the use of a general motion model for smooth camera movement, and solutions for monocular feature initialization and feature orientation estimation. Together, these add up to an extremely efficient and robust algorithm which runs at 30 Hz with standard PC and camera hardware. This work extends the range of robotic systems in which SLAM can be usefully applied, but also opens up new areas. We present applications of MonoSLAM to real-time 3D localization and mapping for a high-performance full-size humanoid robot and live augmented reality with a hand-held camera. Index Terms—Autonomous vehicles, 3D/stereo scene analysis, tracking. 1

Abstract — This paper 1 presents a method for Simultaneous Localization and Mapping (SLAM) relying on a monocular camera as the only sensor which is able to build outdoor, closedloop maps much larger than previously achieved with such input. Our system, based on the Hierarchical Map approach [1], builds independent local maps in real-time using the EKF-SLAM technique and the inverse depth representation proposed in [2]. The main novelty in the local mapping process is the use of a data association technique that greatly improves its robustness in dynamic and complex environments. A new visual map matching algorithm stitches these maps together and is able to detect large loops automatically, taking into account the unobservability of scale intrinsic to pure monocular SLAM. The loop closing constraint is applied at the upper level of the Hierarchical Map in near real-time. We present experimental results demonstrating monocular SLAM as a human carries a camera over long walked trajectories in outdoor areas with people and other clutter, even in the more difficult case of forward-looking camera, and show the closing of loops of several hundred meters. I.

We present incremental smoothing and mapping (iSAM), a novel approach to the simultaneous localization and mapping problem that is based on fast incremental matrix factorization. iSAM provides an efficient and exact solution by updating a QR factorization of the naturally sparse smoothing information matrix, therefore recalculating only the matrix entries that actually change. iSAM is efficient even for robot trajectories with many loops as it avoids unnecessary fill-in in the factor matrix by periodic variable reordering. Also, to enable data association in real-time, we provide efficient algorithms to access the estimation uncertainties of interest based on the factored information matrix. We systematically evaluate the different components of iSAM as well as the overall algorithm using various simulated and real-world datasets for both landmark and pose-only settings.

Abstract — We present a system for Monocular Simultaneous Localization and Mapping (Mono-SLAM) relying solely on video input. Our algorithm makes it possible to precisely estimate the camera trajectory without relying on any motion model. The estimation is fully incremental: at a given time frame, only the current location is estimated while the previous camera positions are never modified. In particular, we do not perform any simultaneous iterative optimization of the camera positions and estimated 3D structure (local bundle adjustment). The key aspects of the system is a fast and simple pose estimation algorithm that uses information not only from the estimated 3D map, but also from the epipolar constraint. We show that the latter leads to a much more stable estimation of the camera trajectory than the conventional approach. We perform high precision camera trajectory estimation in urban scenes with a large amount of clutter. Using an omnidirectional camera placed on a vehicle, we cover the longest distance ever reported, up to 2.5 kilometers. I.

Abstract — This paper presents a vision-based approach to SLAM in large-scale environments with minimal sensing and computational requirements. The approach is based on a graphical representation of robot poses and links between the poses. Links between the robot poses are established based on odometry and image similarity, then a relaxation algorithm is used to generate a globally consistent map. To estimate the covariance matrix for links obtained from the vision sensor, a novel method is introduced based on the relative similarity of neighbouring images, without requiring distances to image features or multiple view geometry. Indoor and outdoor experiments demonstrate that the approach scales well to large-scale environments, producing topologically correct and geometrically accurate maps at minimal computational cost. Mini-SLAM was found to produce consistent maps in an unstructured, large-scale environment (the total path length was 1.4 km) containing indoor and outdoor passages. I.

The next round of planetary missions will require increased autonomy to enable exploration rovers to travel great distances with limited aid from a human operator. For autonomous operations at this scale, localization and terrain modeling become key aspects of onboard rover functionality. Previous Mars rover missions have relied on odometric sensors such as wheel encoders and inertial measurement units/gyros for on-board motion estimation. While these offer a simple solution, they are prone to wheel-slip in loose soil and drift of biases, respectively. Alternatively, the use of visual landmarks observed by stereo cameras to localize a rover offers a more robust solution but at the cost of increased complexity. Additionally rovers will need to create photo-realistic three-dimensional models of visited sites for autonomous operations on-site and mission planning on Earth. 1.

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Camera-based simultaneous localization and mapping or visual SLAM has received much attention recently. Typically single cameras, multiple cameras in a stereo setup or omni-directional cameras are used. We propose a different approach, where multiple cameras can be mounted on a robot in an arbitrary configuration. Allowing the cameras to face in different directions yields better constraints than single cameras or stereo setups can provide, simplifying the reconstruction of large-scale environments. And in contrast to omni-directional sensors, the available resolution can be focused on areas of interest depending on the application. We describe a sparse SLAM approach that is suitable for real-time reconstruction from such multi-camera configurations. We have implemented the system and show experimental results in a large-scale environment, using a custom made eight-camera rig. I.

We propose to use a multi-camera rig for simultaneous localization and mapping (SLAM), providing flexibility in sensor placement on mobile robot platforms while exploiting the stronger localization constraints provided by omni-directional sensors. In this context, we present a novel probabilistic approach to data association, that takes into account that features can also move between cameras under robot motion. Our approach circumvents the combinatorial data association problem by using an incremental expectation maximization algorithm. In the expectation step we determine a distribution over correspondences by sampling. In the maximization step, we find optimal parameters of a density over the robot motion and environment structure. By summarizing the sampling results in so-called virtual measurements, the resulting optimization simplifies to the equivalent optimization problem for known correspondences. We present results for simulated data, as well as for data obtained by a mobile robot equipped with a multi-camera rig. Key words: localization, mapping, mobile robot, multi-camera rig, omni-directional, SFM

Image-based navigation paradigms have recently emerged as an interesting alternative to conventional modelbased methods in mobile robotics. In this paper, we augment the existing image-based navigation approaches by presenting a novel image-based exploration algorithm. The algorithm facilitates a mobile robot equipped only with a monocular pan-tilt camera to autonomously explore a typical indoor environment. The algorithm infers frontier information directly from the images and displaces the robot towards regions that are informative for navigation. The frontiers are detected using a geometric context-based segmentation scheme that exploits the natural scene structure in indoor environments. In the due process, a topological graph of the workspace is built in terms of images which can be subsequently utilised for the tasks of localisation, path planning and navigation. Experimental results