Despite impressive advances in simultaneous localization and mapping, dense robotic mapping remains challenging due to its inherent nature of being a high-dimensional inference problem. In this paper, we propose a dense semantic robotic mapping technique that exploits sparse Bayesian models, in particular, the relevance vector machine, for highdimensional sequential inference. The technique is ...

This paper presents a method for extracting distinctive textural features from images taken from natural scenes. The aim is to use natural landmarks for navigation in an unexplored environment. Natural features are all different and complex in shape. To be able to use them for navigation, informative representation of these features and a careful selection process is required. The present metho...

We address the problem of robot localization based on eigenspace representations of a collection of views of the workspace, often referred to as appearance based methods. We study several approaches to build such eigenspaces in an incremental fashion (subspace tracking), thereby allowing the simultaneous localization and mapping of a mobile vehicle. We see how different ways of determining whet...

In this article, we present our work to provide a navigation and localization system on a constrained humanoid platform, the NAO robot, without modifying the robot sensors. First we try to implement a simple and light version of classical monocular Simultaneous Localization and Mapping (SLAM) algorithms, while adapting to the CPU and camera quality, which turns out to be insufficient on the pla...

This article presents a robotic dataset collected from the largest underground copper mine in the world. The sensor measurements from a 3D scanning lidar, a 2D radar, and stereo cameras were recorded from an approximately two kilometer traverse of a production-active tunnel. The equipment used and the data collection process is discussed in detail, along with the format of the data. This datase...

This paper discusses algorithms for simultaneous localization and mapping (SLAM). Advantages of each major type of algorithm, probabilistic and incremental, are presented. Hybrid approaches and their possibilities are discussed. An explanation of a proper algorithm selection method has been provided. The example of an urban search and rescue (USAR) robot is used to show this selection method. T...

To operate in natural environmental settings, autonomous mobile robots need more than just the ability to navigate in the world, react to perceived situations or follow pre-determined strategies: they must be able to plan and to adapt those plans according to the robot’s capabilities and the situations encountered. Navigation, simultaneous localization and mapping, perception, motivations, plan...

The paradigm case for robotic mapping assumes large quantities of sensory information which allow the use of relatively weak priors. In contrast, the present study considers the mapping problem in environments where only sparse, local sensory information is available. To compensate for these weak likelihoods, we make use of strong hierarchical object priors. Hierarchical models were popular in ...

In this article we present an algorithm to estimate the orientation of a robot relative to an orientation specified at the beginning of the process. This is done by computing the rotation of the robot between successive panoramic images, grabbed on the robot while it moves, using a sub-symbolic method to match the images. The context of the work is Simultaneous Localisation And Mapping (SLAM) i...

Recently, there has been increasing interest in studying the task coordination of aerial and ground robots. When a robot begins navigation in an unknown area, it has no information about the surrounding environment. Accordingly, for robots to perform tasks based on location information, they need a simultaneous localization and mapping (SLAM) process that uses sensor information to draw a map o...