Abstract—While the most accurate solution to off-line structure from motion (SFM) problems is undoubtedly to extract as much correspondence information as possible and perform global optimisation, sequential methods suitable for live video streams must approximate this to fit within fixed computational bounds. Two quite different approaches to real-time SFM — also called monocular SLAM (Simultaneous Localisation and Mapping) — have proven successful, but they sparsify the problem in different ways. Filtering methods marginalise out past poses and summarise the information gained over time with a probability distribution. Keyframe methods retain the optimisation approach of global bundle adjustment, but computationally must select only a small number of past frames to process. In this paper we perform the first rigorous analysis of the relative advantages of filtering and sparse optimisation for sequential monocular SLAM. A series of experiments in simulation as well using a real image SLAM system were performed by means of covariance propagation and Monte Carlo methods, and comparisons made using a combined cost/accuracy measure. With some well-discussed reservations, we conclude that while filtering may have a niche in systems with low processing resources, in most modern applications keyframe optimisation gives the most accuracy per unit of computing time. I.

Abstract—We describe a new formulation of appearance-only SLAM suitable for very large scale navigation. The system navigates in the space of appearance, assigning each new observation to either a new or previously visited location, without reference to metric position. The system is demonstrated performing reliable online appearance mapping and loop closure detection over a 1,000 km trajectory, with mean filter update times of 14 ms. The 1,000 km experiment is more than an order of magnitude larger than any previously reported result. The scalability of the system is achieved by defining a sparse approximation to the FAB-MAP model suitable for implementation using an inverted index. Our formulation of the problem is fully probabilistic and naturally incorporates robustness against perceptual aliasing. The 1,000 km data set comprising almost a terabyte of omni-directional and stereo imagery is available for use, and we hope that it will serve as a benchmark for future systems. I.

Abstract — In robotics, appearance-based topological map building consists in infering the topology of the environment explored by a robot from its sensor measurements. In this paper, we propose a vision-based framework that considers this data association problem from a loop-closure detection perspective in order to correctly assign each measurement to its location. Our approach relies on the visual bag of words paradigm to represent the images and on a discrete Bayes filter to compute the probability of loop-closure. We demonstrate the efficiency of our solution by incremental and real-time consistent map building in an indoor environment and under strong perceptual aliasing conditions using a single monocular wide-angle camera. I.

This paper describes a body of work aimed at extending the reach of mobile navigation and mapping. We describe how running topological and metric mapping and pose estimation processes concurrently, using vision and laser ranging, has produced a full six-degree-of-freedom outdoor navigation system. It is capable of producing intricate 3D maps over many kilometers and in real time. We consider issues concerning the intrinsic quality of the built maps and describe our progress towards adding semantic labels to maps via scene de-construction and labeling. We show how our choices of representation, inference methods and use of both topological and metric techniques naturally allow us to fuse maps built from multiple sessions with no need for manual frame alignment or data association. I.

environments

Recognizing the location and orientation of a mobile device from captured images is a promising application of image retrieval algorithms. Matching the query images to an existing georeferenced database like Google Street View enables mobile search for location related media, products, and services. Due to the rapidly changing field of view of the mobile device caused by constantly changing user attention, very low retrieval times are essential. These can be significantly reduced by performing the feature quantization on the handheld and transferring compressed Bag-of-Feature vectors to the server. To cope with the limited processing capabilities of handhelds, the quantization of high dimensional feature descriptors has to be performed at very low complexity. To this end, we introduce in this paper the novel Multiple Hypothesis Vocabulary Tree (MHVT) as a step towards real-time mobile location recognition. The MHVT increases the probability of assigning matching feature descriptors to the same visual word by introducing an overlapping buffer around the separating hyperplanes to allow for a soft quantization and an adaptive clustering approach. Further, a novel framework is introduced that allows us to integrate the probability of correct quantization in the distance calculation using an inverted file scheme. Our experiments demonstrate that our approach achieves query times reduced by up to a factor of 10 when compared to the state-of-the-art.

Abstract — Gaussian Mixture Regression has been shown to be a powerful and easy-to-tune regression technique for imitation learning of constrained motor tasks in robots. Yet, current formulations are not suited when one wants a robot to learn incrementally and online a variety of new contextdependant tasks whose number and complexity is not known at programming time, and when the demonstrator is not allowed to tell the system when he introduces a new task (but rather the system should infer this from the continuous sensorimotor context). In this paper, we show that this limitation can be addressed by introducing an Incremental, Local and Online variation of Gaussian Mixture Regression (ILO-GMR) which successfully allows a simulated robot to learn incrementally and online new motor tasks through modelling them locally as dynamical systems, and able to use the sensorimotor context to cope with the absence of categorical information both during demonstrations and when a reproduction is asked to the system. Moreover, we integrate a complementary statistical technique which allows the system to incrementally learn various tasks which can be intrinsically defined in different frames of reference, which we call framings, without the need to tell the system which particular framing should be used for each task: this is inferred automatically by the system. I.

Abstract — We present a vision-based navigation and localization system using two biologically-inspired scene understanding models which are studied from human visual capabilities: (1) Gist model which captures the holistic characteristics and layout of an image and (2) Saliency model which emulates the visual attention of primates to identify conspicuous regions in the image. Here the localization system utilizes the gist features and salient regions to accurately localize the robot, while the navigation system uses the salient regions to perform visual feedback control to direct its heading and go to a user-provided goal location. We tested the system on our robot, Beobot2.0, in an indoor and outdoor environment with a route length of 36.67m (10,890 video frames) and 138.27m (28,971 frames), respectively. On average, the robot is able to drive within 3.68cm and 8.78cm (respectively) of the center of the lane. I.

Abstract—We address the problem of simultaneous localization and mapping (SLAM) by combining visual loop-closure detection with metrical information given by a robot odometry. The proposed algorithm extends a purely appearance-based loopclosure detection method based on bags of visual words [1] which is able to detect when the robot has returned back to a previously visited place. An efficient optimization algorithm is used to integrate odometry information in this method to generate a consistent topo-metrical map. The resulting algorithm which only requires a monocular camera and odometry data and is simple, and robust without requiring any a priori information on the environment. Keywords—SLAM, monocular vision, odometry, mobile robot, topo-metrical map. I.

Abstract — We address the problem of simultaneous localization and mapping by combining visual loop-closure detection with metrical information given by the robot odometry. The proposed algorithm builds in real-time topo-metric maps of an unknown environment, with a monocular or omnidirectional camera and odometry gathered by motors encoders. A dedicated improved version of our previous work on purely appearance-based loop-closure detection [1] is used to extract potential loop-closure locations. Potential locations are then verified and classified using a new validation stage. The main contributions we bring are the generalization of the validation method for the use of monocular and omnidirectional camera with the removal of the camera calibration stage, the inclusion of an odometry-based evolution model in the Bayesian filter which improves accuracy and responsiveness, and the addition of a consistent metric position estimation. This new SLAM method does not require any calibration or learning stage (i.e. no a priori information about environment). It is therefore fully incremental and generates maps usable for global localization and planned navigation. This algorithm is moreover well suited for remote processing and can be used on toy robots with very small computational power.