Cristina Losada

Guidance of a mobile robot using an array of static cameras located in the environment

Abstract This paper presents a new proposal for positioning and guiding mobile robots in indoor environments. The proposal is based on the information provided by static cameras located in the movement environment. This proposal falls within the scope of what are known as intelligent environments; in this case, the environment is provided with cameras that, once calibrated, allow the position of the robots to be obtained. Based on this information, control orders for the robots can be generated using a radio frequency link. In order to facilitate identification of the robots, even under extremely adverse ambient lighting conditions, a beacon consisting of four circular elements constructed from infrared diodes is mounted on board the robots. In order to identify the beacon, an edge detection process is carried out. This is followed by a process that, based on the algebraic distance, obtains the estimated ellipses associated with each element of the beacon. Once the beacon has been identified, the coordinates of the centroids for the elements that make up the beacon are obtained on the various image planes. Based on these coordinates, an algorithm is proposed that takes into account the standard deviation of the error produced in the various cameras in ascertaining the coordinates of the beacon’s elements. An odometric system is also used in guidance that, in conjunction with a Kalman Filter, allows the position of the robot to be estimated during the time intervals required to process the visual information provided by the cameras.

Multi-Camera Sensor System for 3D Segmentation and Localization of Multiple Mobile Robots

This paper presents a method for obtaining the motion segmentation and 3D localization of multiple mobile robots in an intelligent space using a multi-camera sensor system. The set of calibrated and synchronized cameras are placed in fixed positions within the environment (intelligent space). The proposed algorithm for motion segmentation and 3D localization is based on the minimization of an objective function. This function includes information from all the cameras, and it does not rely on previous knowledge or invasive landmarks on board the robots. The proposed objective function depends on three groups of variables: the segmentation boundaries, the motion parameters and the depth. For the objective function minimization, we use a greedy iterative algorithm with three steps that, after initialization of segmentation boundaries and depth, are repeated until convergence.

Localization of Mobile Robots Using Odometry and an External Vision Sensor

This paper presents a sensor system for robot localization based on the information obtained from a single camera attached in a fixed place external to the robot. Our approach firstly obtains the 3D geometrical model of the robot based on the projection of its natural appearance in the camera while the robot performs an initialization trajectory. This paper proposes a structure-from-motion solution that uses the odometry sensors inside the robot as a metric reference. Secondly, an online localization method based on a sequential Bayesian inference is proposed, which uses the geometrical model of the robot as a link between image measurements and pose estimation. The online approach is resistant to hard occlusions and the experimental setup proposed in this paper shows its effectiveness in real situations. The proposed approach has many applications in both the industrial and service robot fields.

Stereo Vision Tracking of Multiple Objects in Complex Indoor Environments

This paper presents a novel system capable of solving the problem of tracking multiple targets in a crowded, complex and dynamic indoor environment, like those typical of mobile robot applications. The proposed solution is based on a stereo vision set in the acquisition step and a probabilistic algorithm in the obstacles position estimation process. The system obtains 3D position and speed information related to each object in the robot’s environment; then it achieves a classification between building elements (ceiling, walls, columns and so on) and the rest of items in robot surroundings. All objects in robot surroundings, both dynamic and static, are considered to be obstacles but the structure of the environment itself. A combination of a Bayesian algorithm and a deterministic clustering process is used in order to obtain a multimodal representation of speed and position of detected obstacles. Performance of the final system has been tested against state of the art proposals; test results validate the authors’ proposal. The designed algorithms and procedures provide a solution to those applications where similar multimodal data structures are found.

Dedicated smart IR barrier for obstacle detection in railways

This work presents an intelligent system, which allows to detect obstacles in railways, based on optical emitters. The sensorial system is based on a barrier of emitters and another of receivers, placed each one of them at one side of the railway. Apart from the disposition of the sensorial system, a codification method of the emission is also presented in order to detect the reception or the non-reception of transmissions between an emitter and a receiver. Obstacle detection is carried out by the lack of the reception in the detectors. A solution is proposed to reduce the number of false alarms related to these systems, by taking advantage of the high redundancy in the measurements. A high reliability under adverse conditions is achieved with the developed system, being possible to detect the presence of obstacles, and to inform about their position.

Efficient Multisensory Barrier for Obstacle Detection on Railways

On current railway systems, it is becoming ever more necessary to install safety elements to avoid accidents. One of the causes that can provoke serious accidents is the existence of obstacles on the tracks, either fixed or mobile. In this paper, a multisensory system that can inform the monitoring system about the existence of obstacles is proposed. The system for obstacle detection consists of two emitting and receiving barriers, which are placed on opposing sides of the railway, respectively, and use infrared and ultrasonic sensors, thus establishing different optical and acoustic links between them. The interruption of one or several links should produce an alarm. However, even without the existence of objects, degradation of links could occur due to atmospheric attenuation, solar radiation, etc., also producing an activation of the alarm system. Since detection is based on the lack of radiation in the detectors, the use of complementary sensors for the same task is justified. Since the minimum size of an object for which an alarm is required to be generated is 50 × 50 × 50 cm, in some situations, several links are interrupted; however, alarms should not be generated. Typical cases are the flight of leaves or the movement of small animals in the scanned area. To avoid alarm activation in such situations, this paper proposes the combined use of diverse techniques of data fusion, based on fuzzy logic and the Dempster-Shafer theory of evidence, to validate the existence of objects, providing a highly reliable detection system.

Advanced Multisensorial Barrier for Obstacle Detection

In the current railway systems, it is becoming more necessary to have safety elements in order to avoid accidents. One of the causes that can provoke serious accidents is the existence of obstacles in railways, either fixed or mobiles. In this work, a multisensorial system is proposed in order to inform the monitoring system of the existence of obstacle. The use of different sensors for the same task is justified by the high degree of reliability needed in these environments, where the safety is fundamental.

Adaptive threshold for robust segmentation of mobile robots from visual information of their own movement

In this work, a solution for robust motion segmentation of mobile robots is presented. Motion segmentation is obtained from the images acquired by a calibrated camera which is located in a fixed position in the environment where the robots are moving, and without incorporating invasive landmarks on board the robots. The proposal is based on the minimization of an objective function that depends on three groups of variables: the segmentation boundaries, the 3D rigid motion parameters (components of linear and angular velocity) and depth (distance to the camera). For the objective function minimization, we use a greedy algorithm which, after initialization, consists of three iterative steps. The accuracy in the results and also the processing time are closely related to the initial values of the involved variables. GPCA technique is used for curve initialization, comparing the reconstruction error with a threshold. Two approaches (fixed and adaptive) are proposed to set that threshold. The experimental tests carried out have proved that the proposed adaptive threshold increases, notably, the robustness of the system against lighting changes.

Optimal estimation techniques to reduce false alarms in railway obstacle detection

This work presents some methods to reduce false alarms in railway obstacle detection. The sensorial system is based on one barrier of infrared emitters and another of receivers, placed on opposing sides of the railway. Obstacle detection is achieved by the lack of reception in the detectors. On the one hand, the efficiency of the system is achieved with the geometrical distribution of the sensorial system and the codification used in the emitting and receiving stages. On the other hand, optimal estimation techniques have been proposed to avoid false alarms, based on Kalman and Hinfin filtering. Principal component analysis is developed to validate the obstacle detection, and to improve the accuracy of the system. A high reliability under adverse conditions is obtained with the barrier, it being possible to detect the presence of obstacles, and to report on their position

Identification and tracking of robots in an intelligent space using static cameras and an XPFCP

This paper tackles the problem of identification and tracking of multiple robots in an intelligent space using an array of cameras placed in fixed positions within the environment. Several types of agent can be found in an intelligent space: controlled agents (mobile robots) and uncontrolled ones (users and obstacles). The information transferred between the controlled agents and the intelligent space is limited to the control commands sent to the robots and the measurements of the odometers received from the robots. The proposed solution allows the localization of mobile agents, even if they are not robots; however, we have focused on the controlled agents. The proposal does not require prior knowledge or invasive landmarks on board the robots. It starts from the segmentation of different agents in motion that allows obtaining the boundaries of all robots and an estimation of all 3D points that define those boundaries. Then, the identification of the robots is obtained by comparing the components of the linear velocity estimated by the motion segmentation algorithm and received from the odometers. In order to track the robots, an eXtended Particle Filter with Classification Process (XPFCP) is employed. Several experimental tests have been carried out, and the results obtained validate the proposal.