This paper presents a novel approach to the multi-vehicle Simultaneous Localisation and Mapping (SLAM) problem that exploits the manner in which observations are fused into the global map of the environment to manage the computational complexity of the algorithm and improve the data association process. Rather than incorporating every observation directly into the global map of the environment,...

A fundamental task for reasoning with preferences is the following: given input preference information from a user, and outcomes α and β, should we infer that the user will prefer α to β? For CP-nets and related comparative preference formalisms, inferring a preference of α over β using the standard definition of derived preference appears to be extremely hard, and has been proved to be PSPACEc...

Place recognition is an essential component of any Simultaneous Localization and Mapping (SLAM) system. Correct place recognition is a difficult perception task in cases where there is significant appearance change as the same place might look very different in the morning and at night or over different seasons. This work addresses place recognition using a two-step (generative and discriminati...

This paper introduces a new approach to SLAM which combines an Information Filter and a non linear optimizer. The basic idea of the suggested technique is to use the Information Filter when the system non linearities are negligible, and to switch to the use of the non linear optimizer when the non linearities are not negligible. Extensive simulations are provided in order to evaluate the perfor...

Current mobility models for simultaneous mobility have their convolution in designing simultaneous movement where mobile nodes (MNs) travel randomly from the two adjacent cells at the same time and also have their complexity in the measurement of the occurrences of simultaneous handover. Simultaneous mobility problem incurs when two of the MNs start handover approximately at the same time. As S...

This article deals with the divergence of the Kalman filter when used on rational non-linear observation functions in the Visual SLAM framework. The context objective is to localize a vehicle and simultaneously to build a map according to environment perceived by a camera. There are many ways to fuse all data from sensors and the usual one is the Kalman filter. A main problem of this approach i...

Continuous, real-time mapping of an environment using a camera requires a constanttime estimation engine. This rules out optimal global solving such as bundle adjustment. In this article, we investigate the precision that can be achieved with only local estimation of motion and structure provided by a stereo pair. We introduce a simple but novel representation of the environment in terms of a s...

The paper describes PECAS, an architecture for intelligent systems, and its application in the Explorer, an interactive mobile robot. PECAS is a new architectural combination of information fusion and continual planning. PECAS plans, integrates and monitors the asynchronous flow of information between multiple concurrent systems. Information fusion provides a suitable intermediary to robustly c...

This paper addresses the problem of simultaneous localization and mapping (SLAM) for teams of collaborating vehicles where the communication bandwidth is limited. We present a novel SLAM algorithm that enables multiple vehicles to acquire a joint map, but which can cope with arbitrary latency and bandwidth limitations such as typically found in airborne vehicle applications. The key idea is to ...

In this paper we outline how we seek to augment existing mapping technology with visual competencies designed to enable Simultaneous Localization and Mapping (SLAM) in outdoor settings. We briefly describe the notion of visual similarity between scenes and discuss how it can be applied to scene labelling, loop closure and the definition of distinctive places. However, the main questions are, ho...