The problem of concurrent mapping and localization has received considerable attention in the mobile robotics community. Existing approaches can largely be grouped into two distinct paradigms: topological and metric. This paper proposes a method that integrates both. It poses the mapping problem as a statistical maximum likelihood problem, and devises an efficient algorithm for search in likeli...

The problem of map building is the problem of determining the location of entities-of-interest in a global frame of reference. Over the last years, probabilistic methods have shown to be well suited for dealing with the uncertainties involved in mobile robot map building. In this paper we introduce a general probabilistic approach to concurrent mapping and localization. This method poses the ma...

We examine the problem of minimizing uncertainty in the automated construction of a visual map of an unknown environment. Our work is motivated by the idea that a robot’s exploration policy can impact the accuracy of the resulting map, and we seek to examine the behavior of a set of policies that exhibit a trade-off between accuracy and efficiency. We are further motivated by the specific requi...

The UvA Rescue Team will participate in the RoboCup Rescue Simulation League by employing a Simultaneous Planning, Localization and Mapping (SPLAM) approach. This approach augments traditional SLAM approaches with Planning in order to accomplish the set tasks. In this approach SLAM is modeled as one of the potential subgoals that may enable completion of the overall task. The benefit of this ap...

Autonomous vehicles require the ability to build maps of an unknown environment while concurrently using these maps for navigation. Current algorithms for this concurrent mapping and localization (CML) problem have been implemented for single vehicles, but do not account for extra positional information available when multiple vehicles operate simultaneously. Multiple vehicles have the potentia...

This paper discusses localisation and mapping techniques based on a single camera. After introducing the given problem, which is known as monocular SLAM, a new camera agnostic monocular SLAM system (CAM-SLAM) is presented. It was developed within the scope of this work and is inspired by recently proposed SLAM-methods. In contrast to most other systems, it supports any central camera model such...

We propose a new algorithm for simultaneous localization and figure-ground segmentation where coupled region-edge shape priors are involved with two different but complementary roles. We resort to a segmentation-based hypothesisand-test paradigm to solve the problem, where the region prior is used to form a segmentation and the edge prior is used to evaluate the validity of the formed segmentat...

Local Bundle Adjustments were recently introduced for visual SLAM (Simultaneous Localization and Mapping). In Monocular Visual SLAM, the scale factor is not observable and the reconstruction scale drifts as time goes by. On long trajectory, this problem makes absolute localisation not usable. To overcome this major problem, data fusion is a possible solution. In this paper, we describe Weighted...

As researchers have pushed the limits of what can be accomplished by a single robot operating in a known or unknown environment, a greater emphasis has been placed on the utilization of mobile multi-robotic systems to accomplish various objectives. In transitioning from a robot-centric approach to a system-centric approach, considerations must be made for the computational and communicative asp...

This paper describes a method proposed for the detection, the tracking and the identification of mobile objects, detected from a mobile camera, typically a camera embedded on a robot. A global architecture is presented, using only vision, in order to solve simultaneously several problems: the camera (or vehicle) Localization, the environment Mapping and the Detection and Tracking of Moving Obje...