Finding the camera pose is an important step in many egocentric video applications. It has been widely reported that, state of the art SLAM algorithms fail on egocentric videos. In this paper, we propose a robust method for camera pose estimation, designed specifically for egocentric videos. In an egocentric video, the camera views the same scene point multiple times as the wearer's head sweeps back and forth. We use this specific motion profile to perform short loop closures aligned with wearer's footsteps. For egocentric videos, depth estimation is usually noisy. In an important departure, we use 2D computations for rotation averaging which do not rely upon depth estimates. The two modification results in much more stable algorithm as is evident from our experiments on various egocentric video datasets for different egocentric applications. The proposed algorithm resolves a long standing problem in egocentric vision and unlocks new usage scenarios for future applications.
In this paper we present a loop closure method for a handheld single-camera SLAM system based on our previous work on relocalization. By finding correspondences between the current image and the map, our system is able to reliably detect loop closures. We compare our algorithm to existing techniques for loop closure in single-camera SLAM based on both image-to-image and map-to-map correspondences and discuss both the reliability and suitability of each algorithm in the context of monocular SLAM.  This paper describes a probabilistic approach to the problem of recognizing places based on their appearance. The system we present is not limited to localization, but can determine that a new observation comes from a previously unseen place, and so augment its map. Effectively this is a SLAM system in the space of appearance. Our probabilistic approach allows us to explicitly account for perceptual aliasing in the environment—identical but indistinctive observations receive a low probability of having come from the same place. We achieve this by learning a generative model of place appearance. By partitioning the learning problem into two parts, new place models can be learned online from only a single observation of a place. The algorithm complexity is linear in the number of places in the map, and is particularly suitable for online loop closure detection in mobile robotics. Structure-from-motion (SfM) is a well-studied problem in the computer vision field and is of particular interest for aerial imaging applications like mapping, terrain modeling, crop monitoring, etc. With the current rapid growth in the commercial UAV and small satellite markets, aerial SfM is becoming even more important. In recent years, free and open source software has enabled almost anyone to apply SfM to their data at no cost. Existing free packages are oriented toward processing unordered collections of photographs and are less efficient at processing ordered collections or video. They are also prone to failure with nearly planar scenes that often arise in aerial photography. While commercial solutions for aerial SfM exist, they are proprietary and expensive. This paper presents a new open source software toolkit named MAP-Tk that targets SfM for aerial video. It exploits temporal continuity and the aerial nature of the data to speed up and improve feature tracking, loop closure, and bundle adjustment. The system is highly configurable and modular. Dynamic plugins provide state-of-the art algorithms from other open source projects like OpenCV, VXL, and Ceres Solver. This paper presents the modular system design, user interface, novel algorithms exploiting aerial video, and results on various aerial data sets. This paper 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.