Robustness and safety are crucial properties for the real-world application of autonomous vehicles. One of the most critical components of any autonomous system is localisation. During the last 20 years there has been significant progress in this area with the introduction of very efficient algorithms for mapping, localisation and SLAM. Many of these algorithms present impressive demonstrations for a particular domain, but fail to operate reliably with changes to the operating environment. The aspect of robustness has not received enough attention and localisation systems for self-driving vehicle applications are seldom evaluated for their robustness. In this paper we propose novel metrics to effectively quantify localisation robustness with or without an accurate ground truth. The experimental results present a comprehensive analysis of the application of these metrics against a number of well known localisation strategies.
@cite_13 proposed an algorithm for computing visual map quality. This method is able to predict localisation quality at a given map query point. It is not a metric capable of gauging robustness in experimental data and does not generalizes to many localisation systems and sensors. Map quality in @cite_13 is an indicator or prediction for localisation performance. The value varies with the parameterisation or weighting of the evaluation function, and its usage is restricted to visual landmark based localisation.
@cite_13
2043732461 2737874330 1985434019 2789806088
In this paper we present a prototype system for image based localization in urban environments. Given a database of views of city street scenes tagged by GPS locations, the system computes the GPS location of a novel query view. We first use a wide-baseline matching technique based on SIFT features to select the closest views in the database. Often due to a large change of viewpoint and presence of repetitive structures, a large percentage of matches (> 50 ) are not correct correspondences. The subsequent motion estimation between the query view and the reference view, is then handled by a novel and efficient robust estimation technique capable of dealing with large percentage of outliers. This stage is also accompanied by a model selection step among the fundamental matrix and the homography. Once the motion between the closest reference views is estimated, the location of the query view is then obtained by triangulation of translation directions. Approximate solutions for cases when triangulation cannot be obtained reliably are also described. The presented system is tested on the dataset used in ICCV 2005 Computer Vision Contest and is shown to have higher accuracy than previous reported results. A variety of end-user devices involving keypoint-based mapping systems are about to hit the market e.g. as part of smartphones, cars, robotic platforms, or virtual and augmented reality applications. Thus, the generated map data requires automated evaluation procedures that do not require experienced personnel or ground truth knowledge of the underlying environment. A particularly important question enabling commercial applications is whether a given map is of sufficient quality for localization. This paper proposes a framework for predicting localization performance in the context of visual landmark-based mapping. Specifically, we propose an algorithm for predicting performance of vision-based localization systems from different poses within the map. To achieve this, a metric is defined that assigns a score to a given query pose based on the underlying map structure. The algorithm is evaluated on two challenging datasets involving indoor data generated using a handheld device and outdoor data from an autonomous fixed-wing unmanned aerial vehicle (UAV). Using these, we are able to show that the score provided by our method is highly correlated to the true localization performance. Furthermore, we demonstrate how the predicted map quality can be used within a belief based path planning framework in order to provide reliable trajectories through high-quality areas of the map. Accurate, reliable, and affordable vehicle localization is one important task in current automotive research activities. It enables technologies like cooperative systems or enhanced map based assistance systems. There are a wide variety of approaches to reach this higher accuracy. The algorithm presented in this paper utilizes image landmarks in combination with a low-cost Global Navigation Satellite System (GNSS) receiver and vehicle odometry to achieve this. While similar approaches often extract features from camera images and match those features with map information, the algorithm presented in this work directly transforms map feature data, creating a image of map features, like the camera would see it. The evaluation of this image prediction uses the coherency value, which is derived from the structure tensor. By predicting the whole image, the incorporation of the map information is moved from feature level to signal level. The likelihood models used for the evaluation of the coherency image are derived from real, manually labeled data. We present promising results of a test drive in an area with complex intersections. Those results are compared to ground truth data. We describe a novel approach to image based localisation in urban environments using semantic matching between images and a 2-D map. It contrasts with the vast majority of existing approaches which use image to image database matching. We use highly compact binary descriptors to represent semantic features at locations, significantly increasing scalability compared with existing methods and having the potential for greater invariance to variable imaging conditions. The approach is also more akin to human map reading, making it more suited to human-system interaction. The binary descriptors indicate the presence or not of semantic features relating to buildings and road junctions in discrete viewing directions. We use CNN classifiers to detect the features in images and match descriptor estimates with a database of location tagged descriptors derived from the 2-D map. In isolation, the descriptors are not sufficiently discriminative, but when concatenated sequentially along a route, their combination becomes highly distinctive and allows localisation even when using non-perfect classifiers. Performance is further improved by taking into account left or right turns over a route. Experimental results obtained using Google StreetView and OpenStreetMap data show that the approach has considerable potential, achieving localisation accuracy of around 85 using routes corresponding to approximately 200 meters.