Indoor localization for mobile industrial robots is a crucial step toward an autonomous system. A mobile robot needs a reliable and robust localization system to achieve its task autonomously. A reasonable estimate of the robot’s state can be achieved through Visual Odometry (VO); however, with dynamic objects in the scene, classical VO approaches need to detect and filter these moving objects. Alternatively, we can use an up-facing camera to track the movement with respect to the ceiling, which represents a static and invariant space. This paper presents Ceiling-DSO, an indoor ceiling-vision (CV) system based on Direct Sparse Odometry (DSO). We take advantage of the generic formulation of DSO to avoid making assumptions about the observable shapes or landmarks on the ceiling, making the method generic and applicable to multiple ceiling types. We built a ceiling-vision dataset in a real-world scenario; we then used it to test our approach with different DSO parameters to identify the best fit for robot pose estimation. This paper provides a qualitative and quantitative analysis of the obtained results that showed an acceptable error rate compared to the ground truth.

In this paper, an evolutionary scan-matching approach is proposed to solve an optimization issue in simultaneous localization and mapping (SLAM). A rich literature has been invested in this direction, however, most of the proposed approaches lack fast convergence and simplicity regarding the optimization process, which should directly affect the accuracy of the environment’s map and the estimated pose. It is a line of research that is always active, offering various solutions to this issue. Among many SLAM methods, the normal distributions transform approach (NDT) has shown high performances, where numerous works have been published up to date and many studies demonstrate its efficiency wrt other methods. Nevertheless, few works have been interested to solve the optimization issue. The proposed solution is based on NDT scan- matching using particle swarm optimization (PSO) and it is dubbed NDT-PSO. The main contribution is to solve the pose estimation problem based on PSO and iterative NDT maps. The performances of the NDT-PSO approach have been proven in real experiments performed on a car-like mobile robot in both static and dynamic environments. NDT-PSO is tested for different swarm sizes, and the results show that 70 particles are more than enough to find the best particle while avoiding local minima even in loop closing. The algorithm is also suitable for real time applications, with an average runnnig time of 145ms for 70 particles and 70 iterations of the optimization process. This value can be further reduced using fewer particles and iterations. The accuracy of the proposed approach is also evaluated wrt other SLAM methods widely used among the robot operating system community and it has been shown that NDT-PSO outperforms these algorithms.

This paper deals with the problem of simultaneous localization and mapping (SLAM). Providing both accurate environment’s map and pose estimation is necessary to correctly navigate, which is a key issue for a mobile robot interacting with human beings. It is a line of research that is always active, offering various solutions to this issue. Nevertheless, among many SLAM methods, Normal Distributions Transform (NDT) has shown high performances, where numerous works have been published up to date and many studies demonstrate its efficiency wrt to other methods. In this paper a new NDT based SLAM method using Particle Swarm Optimization called NDT- PSO is proposed. The main contribution is to invest the bio- inspired approach PSO to solve pose estimation problem based on iterative NDT maps. Real experiments have been performed on a car-like mobile robot to confirm the performances of NDT-PSO approach and its efficiency in both static and dynamic environments.

Warehouses and industrial sites are getting more and more interest in automating their workflow; in such an environment, a robust localization method is required to accomplish safe navigation indoors. One widely used scheme is the usage of custom AGVs and dedicated infrastructures to automate moving goods within the warehouse; however, such a solution needs to modify the infrastructure or to make custom robots that fit the existing infrastructure, which requires an important investment. In this paper, we present and validate the SmartTrolley, a generic, modular, and scalable experimental platform for usage in warehouses and industrial sites; able to localize itself in the environment using a scan matching and EKF based indoor Simultaneous Localization and Mapping (SLAM) algorithm.

Ce travail traite les problèmes de localisation et de cartographie pour un robot mobile de type voiture évoluant dans un environnement urbain inconnu. Le robot est équipé d’un capteur laser de type Sick LMS511 Pro dont les données sont exploitées pour l’accomplissement de ces tâches. Dans une telle situation, les deux problèmes ne peuvent pas être dissociés, c’est pourquoi, nous proposons d’adopter une approche de localisation et cartographie simultanées (SLAM). La solution développée dans ce manuscrit est nommée NDT-PSO. Elle est basée sur la méthode de la transformation de distribution normale (NDT) et la méthode d’optimisation par essaim particulaire (PSO). La NDT est une méthode SLAM dont le principe est de déterminer la transformation géométrique entre deux scans laser successives grâce à des techniques d’alignement. Sa représentation de l’environnement est basée sur la modélisation de tous les points 2D reconstruits à partir d’un scan laser par une collection de distributions normales locales. La méthode PSO est utilisée dans la phase d’optimisation des paramètres de transformation afin de déterminer les poses (positions et orientations) du robot. Les algorithmes proposés sont implémentés en langage Python sous le système ROS et testés sur le robot mobile RobuCar dans le cadre d’un projet de transport urbain intelligent.