Mapping, Localization and Path Planning for Image-based Navigation using Visual Features and Map

Building on progress in feature representations for image retrieval, image-based localization has seen a surge of research interest. Image-based localization has the advantage of being inexpensive and efficient, often avoiding the use of 3D metric maps altogether. That said, the need to maintain a large number of reference images as an effective support of localization in a scene, nonetheless calls for them to be organized in a map structure of some kind. The problem of localization often arises as part of a navigation process. We are, therefore, interested in summarizing the reference images as a set of landmarks, which meet the requirements for image-based navigation. A contribution of this paper is to formulate such a set of requirements for the two sub-tasks involved: map construction and self-localization. These requirements are then exploited for compact map representation and accurate self-localization, using the framework of a network flow problem. During this process, we formulate the map construction and self-localization problems as convex quadratic and second-order cone programs, respectively. We evaluate our methods on publicly available indoor and outdoor datasets, where they outperform existing methods significantly.Comment: CVPR 2019, for implementation see https://github.com/janinethom

Navigation without localisation: reliable teach and repeat based on the convergence theorem

We present a novel concept for teach-and-repeat visual navigation. The proposed concept is based on a mathematical model, which indicates that in teach-and-repeat navigation scenarios, mobile robots do not need to perform explicit localisation. Rather than that, a mobile robot which repeats a previously taught path can simply `replay' the learned velocities, while using its camera information only to correct its heading relative to the intended path. To support our claim, we establish a position error model of a robot, which traverses a taught path by only correcting its heading. Then, we outline a mathematical proof which shows that this position error does not diverge over time. Based on the insights from the model, we present a simple monocular teach-and-repeat navigation method. The method is computationally efficient, it does not require camera calibration, and it can learn and autonomously traverse arbitrarily-shaped paths. In a series of experiments, we demonstrate that the method can reliably guide mobile robots in realistic indoor and outdoor conditions, and can cope with imperfect odometry, landmark deficiency, illumination variations and naturally-occurring environment changes. Furthermore, we provide the navigation system and the datasets gathered at http://www.github.com/gestom/stroll_bearnav.Comment: The paper will be presented at IROS 2018 in Madri

Monocular navigation for long-term autonomy

We present a reliable and robust monocular navigation system for an autonomous vehicle. The proposed method is computationally efficient, needs off-the-shelf equipment only and does not require any additional infrastructure like radio beacons or GPS. Contrary to traditional localization algorithms, which use advanced mathematical methods to determine vehicle position, our method uses a more practical approach. In our case, an image-feature-based monocular vision technique determines only the heading of the vehicle while the vehicle's odometry is used to estimate the distance traveled. We present a mathematical proof and experimental evidence indicating that the localization error of a robot guided by this principle is bound. The experiments demonstrate that the method can cope with variable illumination, lighting deficiency and both short- and long-term environment changes. This makes the method especially suitable for deployment in scenarios which require long-term autonomous operation

Featureless visual processing for SLAM in changing outdoor environments

Vision-based SLAM is mostly a solved problem providing clear, sharp images can be obtained. However, in outdoor environments a number of factors such as rough terrain, high speeds and hardware limitations can result in these conditions not being met. High speed transit on rough terrain can lead to image blur and under/over exposure, problems that cannot easily be dealt with using low cost hardware. Furthermore, recently there has been a growth in interest in lifelong autonomy for robots, which brings with it the challenge in outdoor environments of dealing with a moving sun and lack of constant artificial lighting. In this paper, we present a lightweight approach to visual localization and visual odometry that addresses the challenges posed by perceptual change and low cost cameras. The approach combines low resolution imagery with the SLAM algorithm, RatSLAM. We test the system using a cheap consumer camera mounted on a small vehicle in a mixed urban and vegetated environment, at times ranging from dawn to dusk and in conditions ranging from sunny weather to rain. We first show that the system is able to provide reliable mapping and recall over the course of the day and incrementally incorporate new visual scenes from different times into an existing map. We then restrict the system to only learning visual scenes at one time of day, and show that the system is still able to localize and map at other times of day. The results demonstrate the viability of the approach in situations where image quality is poor and environmental or hardware factors preclude the use of visual features