Xiaolong Yang

Bio: Xiaolong Yang is an academic researcher from George Mason University. The author has contributed to research in topics: Hidden Markov model & Mobile robot. The author has an hindex of 3, co-authored 3 publications receiving 148 citations.

Global localization and relative pose estimation based on scale-invariant features

Abstract: The capability of maintaining the pose of the mobile robot is central for basic navigation and map building tasks. In This work we describe a vision-based hybrid localization scheme based on scale-invariant keypoints. In the first stage the topological localization is accomplished by matching the keypoints detected in the current view with the database of model views. Once the best match has been found, the relative pose between the model view and the current image is recovered. We demonstrate the efficiency of the location recognition approach and present a closed form solution to the relative pose recovery for the case of planar motion and unknown focal length of the camera. The approach is demonstrated on several examples of indoor environments.

Location Recognition and Global Localization Based on Scale-Invariant Keypoints

Abstract: The localization capability of a mobile robot is central to basic navigation and map building tasks. We describe a probabilistic environment model which facilitates global localization scheme by means of location recognition. In the exploration stage the environment is partitioned into several locations, each characterized by a set of scale-invariant keypoints. The descriptors associated with these keypoints can be robustly matched despite the changes in contrast, scale and affine distortions. We demonstrate the efficacy of these features for location recognition, where given a new view the most likely location from which this view came is determined. The misclassifications due to dynamic changes in the environment or inherent location appearance ambiguities are overcome by exploiting the location neighborhood relationships captured by a Hidden Markov Model. We report the recognition performance of this approach in an indoor environment consisting of eighteen locations and discuss the suitability of this approach for a more general class of recognition problems.

Visual Place Recognition: A Survey

Abstract: Visual place recognition is a challenging problem due to the vast range of ways in which the appearance of real-world places can vary. In recent years, improvements in visual sensing capabilities, an ever-increasing focus on long-term mobile robot autonomy, and the ability to draw on state-of-the-art research in other disciplines—particularly recognition in computer vision and animal navigation in neuroscience—have all contributed to significant advances in visual place recognition systems. This paper presents a survey of the visual place recognition research landscape. We start by introducing the concepts behind place recognition—the role of place recognition in the animal kingdom, how a “place” is defined in a robotics context, and the major components of a place recognition system. Long-term robot operations have revealed that changing appearance can be a significant factor in visual place recognition failure; therefore, we discuss how place recognition solutions can implicitly or explicitly account for appearance change within the environment. Finally, we close with a discussion on the future of visual place recognition, in particular with respect to the rapid advances being made in the related fields of deep learning, semantic scene understanding, and video description.

Fast and Incremental Method for Loop-Closure Detection Using Bags of Visual Words

Abstract: In robotic applications of visual simultaneous localization and mapping techniques, loop-closure detection and global localization are two issues that require the capacity to recognize a previously visited place from current camera measurements. We present an online method that makes it possible to detect when an image comes from an already perceived scene using local shape and color information. Our approach extends the bag-of-words method used in image classification to incremental conditions and relies on Bayesian filtering to estimate loop-closure probability. We demonstrate the efficiency of our solution by real-time loop-closure detection under strong perceptual aliasing conditions in both indoor and outdoor image sequences taken with a handheld camera.

Visual-inertial navigation, mapping and localization: A scalable real-time causal approach

Abstract: We describe a model to estimate motion from monocular visual and inertial measurements. We analyze the model and characterize the conditions under which its state is observable, and its parameters are identifiable. These include the unknown gravity vector, and the unknown transformation between the camera coordinate frame and the inertial unit. We show that it is possible to estimate both state and parameters as part of an on-line procedure, but only provided that the motion sequence is â??rich enoughâ??, a condition that we characterize explicitly. We then describe an efficient implementation of a filter to estimate the state and parameters of this model, including gravity and camera-to-inertial calibration. It runs in real-time on an embedded platform. We report experiments of continuous operation, without failures, re-initialization, or re-calibration, on paths of length up to 30 km. We also describe an integrated approach to â??loop-closureâ??, that is the recognition of previously seen locations and the topological re-adjustment of the traveled path. It represents visual features relative to the global orientation reference provided by the gravity vector estimated by the filter, and relative to the scale provided by their known position within the map; these features are organized into â??locationsâ?? defined by visibility constraints, represented in a topological graph, where loop-closure can be performed without the need to re-compute past trajectories or perform bundle adjustment. The software infrastructure as well as the embedded platform is described in detail in a previous technical report.

Outdoor SLAM using visual appearance and laser ranging

Abstract: This paper describes a 3D SLAM system using information from an actuated laser scanner and camera installed on a mobile robot. The laser samples the local geometry of the environment and is used to incrementally build a 3D point-cloud map of the workspace. Sequences of images from the camera are used to detect loop closure events (without reference to the internal estimates of vehicle location) using a novel appearance-based retrieval system. The loop closure detection is robust to repetitive visual structure and provides a probabilistic measure of confidence. The images suggesting loop closure are then further processed with their corresponding local laser scans to yield putative Euclidean image-image transformations. We show how naive application of this transformation to effect the loop closure can lead to catastrophic linearization errors and go on to describe a way in which gross, pre-loop closing errors can be successfully annulled. We demonstrate our system working in a challenging, outdoor setting containing substantial loops and beguiling, gently curving traversals. The results are overlaid on an aerial image to provide a ground truth comparison with the estimated map. The paper concludes with an extension into the multi-robot domain in which 3D maps resulting from distinct SLAM sessions (no common reference frame) are combined without recourse to mutual observation

A visual bag of words method for interactive qualitative localization and mapping

Abstract: Localization for low cost humanoid or animal-like personal robots has to rely on cheap sensors and has to be robust to user manipulations of the robot. We present a visual localization and map-learning system that relies on vision only and that is able to incrementally learn to recognize the different rooms of an apartment from any robot position. This system is inspired by visual categorization algorithms called bag of words methods that we modified to make fully incremental and to allow a user-interactive training. Our system is able to reliably recognize the room in which the robot is after a short training time and is stable for long term use. Empirical validation on a real robot and on an image database acquired in real environments are presented.