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We propose a direct monocular SLAM algorithm based on the Normalised Information Distance (NID) metric. In contrast to current state-of-the-art direct methods based on photometric error minimisation, our information-theoretic NID metric provides robustness to appearance variation due to lighting, weather and structural changes in the scene. We demonstrate successful localisation and mapping across...
Direct visual odometry and Simultaneous Localization and Mapping (SLAM) methods determine camera poses by means of direct image alignment. This optimizes a photometric cost term based on the Lucas-Kanade method. Many recent works use the brightness constancy assumption in the alignment cost formulation and therefore cannot cope with significant illumination changes. Such changes are especially likely...
Considering the situation that performing indoor positioning system on mobile platform mostly relying on additional wireless signal and known environmental information, we propose an autonomous combination positioning method aimed at continuously estimating metric position and pose in an unknown scene. The vision-based SLAM algorithm extracts the image characteristics of the environment for producing...
Simultaneous localization and mapping (SLAM) algorithms allow a single robot to reduce the effects of drifting sensor biases while exploring unknown, GPS-denied environments. To reduce exploration time, a team of robots can build smaller maps in parallel and perform map fusion. Most map fusion techniques require a known relative transformation between coordinate frames. Other techniques rely on inter-robot...
The task of Simultaneous Localization and Mapping (SLAM) is regularly performed in network spaces consisting of a set of corridors connecting locations in the space. Empirical research has demonstrated that such spaces generally exhibit common structural properties relating to aspects such as corridor length. Consequently there exists potential to improve performance through the placement of priors...
Robust, scalable place recognition is a core competency for many robotic applications. However, when revisiting places over and over, many state-of-the-art approaches exhibit reduced performance in terms of computation and memory complexity and in terms of accuracy. For successful deployment of robots over long time scales, we must develop algorithms that get better with repeated visits to the same...
The European V-Charge project seeks to develop fully automated valet parking and charging of electric vehicles using only low-cost sensors. One of the challenges is to implement robust visual localization using only cameras and stock vehicle sensors. We integrated four monocular, wide-angle, fisheye cameras on a consumer car and implemented a mapping and localization pipeline. Visual features and...
SLAM algorithms that can infer a correct map despite the presence of outliers have recently attracted increasing attention. In the context of SLAM, outlier constraints are typically caused by a failed place recognition due to perceptional aliasing. If not handled correctly, they can have catastrophic effects on the inferred map. Since robust robotic mapping and SLAM are among the key requirements...
Inter-robot observation (Rendezvous) strategy is usually adopted to align local maps in multi-robot SLAM system while the initial relative positions are unknown. However, the accuracy of the estimated alignment is often affected by various uncertainty sources. This paper presents a new approach based on the Point-to-Line Iterative Closest Point (PLICP) algorithm to improve the accuracy of the map...
In this paper, a novel variant of the ICP algorithm is proposed for registration of partially overlapping range images. Biunique correspondence is introduced to enhance the performance of ICP by searching multiple closest points. A new kind of outlier is defined, called No-Correspondence (NC) Outlier, which is the point that is not assigned to a biunique correspondence. In order to maintain efficiency,...
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