We present a robust and real-time monocular six degree of freedom visual relocalization system. We use a Bayesian convolutional neural network to regress the 6-DOF camera pose from a single RGB image. It is trained in an end-to-end manner with no need of additional engineering or graph optimisation. The algorithm can operate indoors and outdoors in real time, taking under 6ms to compute. It obtains approximately 2m and 6 degrees accuracy for very large scale outdoor scenes and 0.5m and 10 degrees accuracy indoors. Using a Bayesian convolutional neural network implementation we obtain an estimate of the model's relocalization uncertainty and improve state of the art localization accuracy on a large scale outdoor dataset. We leverage the uncertainty measure to estimate metric relocalization error and to detect the presence or absence of the scene in the input image. We show that the model's uncertainty is caused by images being dissimilar to the training dataset in either pose or appearance.
Although many of the modern SLAM algorithms do not consider localization uncertainty @cite_20 @cite_21 @cite_14 , previous probabilistic algorithms have been proposed. Bayesian approaches include extended Kalman filters and particle filter approaches such as FastSLAM @cite_16 . However these approaches estimate uncertainty from sensor noise models, not the uncertainty of the model to represent the data. Our proposed framework does not assume any input noise but measures the model uncertainty for localization.
@cite_14 @cite_21 @cite_20 @cite_16
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This paper proposes a pose-based algorithm to solve the full Simultaneous Localization And Mapping (SLAM) problem for an Autonomous Underwater Vehicle (AUV), navigating in an unknown and possibly unstructured environment. A probabilistic scan matching technique using range scans gathered from a Mechanical Scanning Imaging Sonar (MSIS) is used together with the robot dead-reckoning displacements. The proposed method utilizes two Extended Kalman Filters (EKFs). The first, estimates the local path traveled by the robot while forming the scan as well as its uncertainty, providing position estimates for correcting the distortions that the vehicle motion produces in the acoustic images. The second is an augmented state EKF that estimates and keeps the registered scans poses. The raw data from the sensors are processed and fused in-line. No priory structural information or initial pose are considered. Also, a method of estimating the uncertainty of the scan matching estimation is provided. The algorithm has been tested on an AUV guided along a 600 m path within a marina environment, showing the viability of the proposed approach. In this paper we study the global localization problem in SLAM: the determination of the vehicle location in a previously mapped environment with no other prior information. We show that, using a grid sampling representation of the configuration space, it is possible to evaluate all vehicle location hypotheses in the environment (up to a certain resolution) with a computational cost that is bilinear: linear both in the number of map features and in the number of sensor measurements. We propose a pairing-driven algorithm that considers only individual measurement-feature pairings and thus, in contrast with current correspondence space algorithms, it avoids searching in the exponential correspondence space. It uses a voting strategy that accumulates evidence for each vehicle location hypothesis, assuring robustness to noise in the sensor measurements and environment models. The general nature of the proposed strategy allows the consideration of different types of features and sensor measurements. Using the popular Victoria Park dataset, we compare its performance with location-driven algorithms where the solution space is usually randomly sampled. We show that the proposed pairing-driven technique is computationally more efficient in proportion to the density of features in the environment. The paper analyzes the properties of the full covariance simultaneous localization and map building problem (SLAM). We prove that, even for the special case of a stationary vehicle (with no process noise) which uses a range-bearing sensor and has non-zero angular uncertainty, the full covariance SLAM algorithm always yields an inconsistent map. We also show, through simulations, that these conclusions appear to extend to a moving vehicle with process noise. However, these inconsistencies only become apparent after several hundred beacon updates. In this paper we investigate the problem of Simultaneous Localization and Mapping (SLAM) for a multi robot system. Relaxing some assumptions that characterize related work we propose an application of Rao-Blackwellized Particle Filters (RBPF) for the purpose of cooperatively estimating SLAM posterior. We consider a realistic setup in which the robots start from unknown initial poses (relative locations are unknown too), and travel in the environment in order to build a shared representation of the latter. The robots are required to exchange a small amount of information only when a rendezvous event occurs and to measure relative poses during the meeting. As a consequence the approach also applies when using an unreliable wireless channel or short range communication technologies (bluetooth, RFId, etc.). Moreover it allows to take into account the uncertainty in relative pose measurements. The proposed technique, which constitutes a distributed solution to the multi robot SLAM problem, is further validated through simulations and experimental tests.