Miguel Juliá

A comparison of path planning strategies for autonomous exploration and mapping of unknown environments

To date, a large number of algorithms to solve the problem of autonomous exploration and mapping has been presented. However, few efforts have been made to compare these techniques. In this paper, an extensive study of the most important methods for autonomous exploration and mapping of unknown environments is presented. Furthermore, a representative subset of these techniques has been chosen to be analysed. This subset contains methods that differ in the level of multi-robot coordination and in the grade of integration with the simultaneous localization and mapping (SLAM) algorithm. These exploration techniques were tested in simulation and compared using different criteria as exploration time or map quality. The results of this analysis are shown in this paper. The weaknesses and strengths of each strategy have been stated and the most appropriate algorithm for each application has been determined.

Multi-robot visual SLAM using a Rao-Blackwellized particle filter

This paper describes an approach to solve the Simultaneous Localization and Mapping (SLAM) problem with a team of cooperative autonomous vehicles. We consider that each robot is equipped with a stereo camera and is able to observe visual landmarks in the environment. The SLAM approach presented here is feature-based, thus the map is represented by a set of 3D landmarks each one defined by a global position in space and a visual descriptor. The robots move independently along different trajectories and make relative measurements to landmarks in the environment in order to jointly build a common map using a Rao-Blackwellized particle filter. We show results obtained in a simulated environment that validate the SLAM approach. The process of observing a visual landmark is simulated in the following way: first, the relative measurement obtained by the robot is corrupted with Gaussian noise, using a noise model for a standard stereo camera. Second, the visual description of the landmark is altered by noise, simulating the changes in the descriptor which may occur when the robot observes the same landmark under different scales and viewpoints. In addition, the noise in the odometry of the robots also takes values obtained from real robots. We propose an approach to manage data associations in the context of visual features. Different experiments have been performed, with variations in the path followed by the robots and the parameters in the particle filter. Finally, the results obtained in simulation demonstrate that the approach is suitable for small robot teams.

A hybrid solution to the multi-robot integrated exploration problem

In this paper we present a hybrid reactive/deliberative approach to the multi-robot integrated exploration problem. In contrast to other works, the design of the reactive and deliberative processes is exclusively oriented to the exploration having both the same importance level. The approach is based on the concepts of expected safe zone and gateway cell. The reactive exploration of the expected safe zone of the robot by means of basic behaviours avoids the presence of local minima. Simultaneously, a planner builds up a decision tree in order to decide between exploring the current expected safe zone or changing to other zone by means of travelling to a gateway cell. Furthermore, the model takes into account the degree of localization of the robots to return to previously explored areas when it is necessary to recover the certainty in the position of the robots. Several simulations demonstrate the validity of the approach.

Estimation of Visual Maps with a Robot Network Equipped with Vision Sensors

In this paper we present an approach to the Simultaneous Localization and Mapping (SLAM) problem using a team of autonomous vehicles equipped with vision sensors. The SLAM problem considers the case in which a mobile robot is equipped with a particular sensor, moves along the environment, obtains measurements with its sensors and uses them to construct a model of the space where it evolves. In this paper we focus on the case where several robots, each equipped with its own sensor, are distributed in a network and view the space from different vantage points. In particular, each robot is equipped with a stereo camera that allow the robots to extract visual landmarks and obtain relative measurements to them. We propose an algorithm that uses the measurements obtained by the robots to build a single accurate map of the environment. The map is represented by the three-dimensional position of the visual landmarks. In addition, we consider that each landmark is accompanied by a visual descriptor that encodes its visual appearance. The solution is based on a Rao-Blackwellized particle filter that estimates the paths of the robots and the position of the visual landmarks. The validity of our proposal is demonstrated by means of experiments with a team of real robots in a office-like indoor environment.

Following route graphs in urban environments

In this paper, an approach is presented that allows a robot to navigate in an urban environment by following natural language route instructions. In this situation, neither maps nor GPS information are available to the robot thus it has to rely solely on the human-given route description and the observations from its sensors. An architecture for solving problems such as navigation on the sidewalk, street direction inference, and environment labeling that arise in this situation is presented. Our initial experiments indicate that the proposed methods enable a robot to safely navigate in urban environments by following abstract route descriptions and reach previously unknown points in a city.

Mechanisms for collaborative teleoperation with a team of cooperative robots

Purpose – This paper aims to present a teleoperation system that allows one to control a group of mobile robots in a collaborative manner. In order to show the capabilities of the collaborative teleoperation system, it seeks to present a task where the operator collaborates with a robot team to explore a remote environment in a coordinated manner. The system implements human‐robot interaction by means of natural language interfaces, allowing one to teleoperate multiple mobile robots in an unknown, unstructured environment. With the supervision of the operator, the robot team builds a map of the environment with a vision‐based simultaneous localization and mapping (SLAM) technique. The approach is well suited for search and rescue tasks and other applications where the operator may guide the exploration of the robots to certain areas in the map.Design/methodology/approach – In opposition with a master‐slave scheme of teleoperation, an exploration mechanism is proposed that allows one to integrate the comma...

Analysis of Map Alignment techniques in visual SLAM systems

In a multi-robot system, in which each of the robots constructs its own local map, it is necessary to perform the fusion of these maps into a global one. This task is normally performed in two different steps: by aligning the maps and then merging the data. This paper focusses on the first step: Map Alignment, which consists in obtaining the transformation between the local maps built independently. In this way, these local maps will have a common reference frame. In this paper, a collection of algorithms for solving the map alignment are analyzed under different conditions of noise in the data and intersection between local maps. This study is performed in a visual SLAM context, in which the robots construct landmark-based maps. The landmarks consist in 3D points captured from the environment and characterized by a visual descriptor.

Improved Omnidirectional Odometry for a View-Based Mapping Approach

This work presents an improved visual odometry using omnidirectional images. The main purpose is to generate a reliable prior input which enhances the SLAM (Simultaneous Localization and Mapping) estimation tasks within the framework of navigation in mobile robotics, in detriment of the internal odometry data. Generally, standard SLAM approaches extensively use data such as the main prior input to localize the robot. They also tend to consider sensory data acquired with GPSs, lasers or digital cameras, as the more commonly acknowledged to re-estimate the solution. Nonetheless, the modeling of the main prior is crucial, and sometimes especially challenging when it comes to non-systematic terms, such as those associated with the internal odometer, which ultimately turn to be considerably injurious and compromise the convergence of the system. This omnidirectional odometry relies on an adaptive feature point matching through the propagation of the current uncertainty of the system. Ultimately, it is fused as the main prior input in an EKF (Extended Kalman Filter) view-based SLAM system, together with the adaption of the epipolar constraint to the omnidirectional geometry. Several improvements have been added to the initial visual odometry proposal so as to produce better performance. We present real data experiments to test the validity of the proposal and to demonstrate its benefits, in contrast to the internal odometry. Furthermore, SLAM results are included to assess its robustness and accuracy when using the proposed prior omnidirectional odometry.

Occupancy grid based graph-SLAM using the distance transform, SURF features and SGD

In this paper, we present a SLAM approach that builds global occupancy-grid maps using laser range data. The method consists of two basic algorithms: a process of finding correspondences and alignments between local sub-maps and a high level optimization algorithm that aligns and builds a global map. The main novelty of the paper is the use of a visual description of the local sub-maps. We propose to use visual features to easy the search of correspondences between different sub-maps. The association of features between different maps gives us transformations between the different key maps. Afterwards, a graph is built using the reference frames as the vertexes and the transformation between key-maps are the edges. Stochastic Gradient Descent (SGD) is next employed to compute a global map. The results show the validity of the proposed algorithm in terms of precision and robustness.

Distributed platform for the control of the Wifibot robot through internet

Abstract This paper presents a distributed platform composed of a team of mobile robots so that they can be controlled by the students through Internet. With this platform, a remote environment is created so that the students can carry out different experiments over several available equipments in the lab, with a flexible schedule. This way, the student can design and test different control algorithms and experience over the mobile robots in the laboratory. Currently, there are 3 kinds of robots available; each one is accessible by the students for monitoring and supervision tasks and also for control tasks using a common interface of communication, in a transparent way from the point of view of the user.