2D SLAM Correction Prediction in Large Scale Urban Environments

International audienceSimultaneous Localization And Mapping (SLAM) is one of the major bricks needed to build truly autonomous mobile robots. The probabilistic formulation of SLAM is based on two models: the motion model and the observation model. In practice, these models, together with the SLAM map representation, do not model perfectly the robot's real dynamics, the sensor measurement errors and the environment. Consequently, systematic errors affect SLAM estimations. In this paper, we propose two approaches to predict corrections to be applied to SLAM estimations. Both are based on the Ensemble Multilayer Perceptron model. The first approach uses successive estimated poses to predict the errors, with no assumptions on the underlying SLAM process or sensor used. The second method is specific to 2D likelihood SLAM approaches, thus, the likelihood distributions are used to predict the corrections, making this second approach independent of the sensor used. We also build a hybrid correction module based on successive estimated poses and the likelihood distributions. The validity of both approaches is evaluated through two experiments using different evaluation metrics and sensor configurations

Look-ahead Proposals for Robust Grid-based SLAM

Summary. Simultaneous Localization and Mapping (SLAM) is one of the classical problems in mobile robotics. The task is to build a map of the environment using on-board sensors while at the same time localizing the robot relative to this map. Rao-Blackwellized particle filters have emerged as a powerful technique for solving the SLAM problem in a wide variety of environments. It is a well-known fact for sampling-based approaches that the choice of the proposal distribution greatly influences the robustness and efficiency achievable by the algorithm. In this paper, we present a significantly improved proposal distribution for grid-based SLAM, which utilizes whole sequences of sensor measurements rather than only the most recent one. We have implemented our system on a real robot and evaluated its performance on standard data sets as well as in hard outdoor settings with few and ambiguous features. Our approach improves the localization accuracy and the map quality. At the same time, it substantially reduces the risk of mapping failures.

Look-ahead proposals for robust grid-based SLAM

Simultaneous Localization and Mapping (SLAM) is one of the classical problems in mobile robotics. The task is to build a map of the environment using on-board sensors while at the same time localizing the robot relative to this map. Rao-Blackwellized particle filters have emerged as a powerful technique for solving the SLAM problem in a wide variety of environments. It is a well-known fact for sampling-based approaches that the choice of the proposal distribution greatly influences the robustness and efficiency achievable by the algorithm. In this paper, we present a significantly improved proposal distribution for grid-based SLAM, which utilizes whole sequences of sensor measurements rather than only the most recent one. We have implemented our system on a real robot and evaluated its performance on standard data sets as well as in hard outdoor settings with few and ambiguous features. Our approach improves the localization accuracy and the map quality. At the same time, it substantially reduces the risk of mapping failures. © 2008 Springer-Verlag Berlin Heidelberg

Machine learning of inteligent mobile robot based on arti ficial neural networks

Унутрашњи транспорт сировина, материјала и готових делова подразумева брзо, ефикасно и економично деловање постављеног транспортног задатка...Material Handling Systems in manufacturing environment imply efficient and economical transport solutions. Automated Guided Vehicles (AGVs) are a common choice made by many companies for Material Handling in manufacturing systems. Nowadays, AGV based internal transport of raw materials, goods and parts is becoming improved with advances in technology. Demands for fast, efficient and reliable transport imply the usage of the flexible AGVs with onboard sensing and special kinds of algorithms needed for daily operation. These transport solutions can be modified and enhanced by applying advanced methods and technologies. New generation of internal transport systems should operate autonomously, without direct human control. Level of development of mobile robots insures reliability and efficiency needed for dayily operations within manufacturing environemnt. In this thesis, the implementation of mobile robots for internal transport within Material Handling System is analyzed and new solutions are proposed. Focus of research efforts is devoted to the ability to estimate position and orientation of mobile robot within manufacturing environment using newly developed algorithms and sensory information. Simultaneous localization (of the mobile robot) and mapping (of the working environment) is one of the most important problems in mobile robotics community. The soultion to this problem insures autonomous navigation and henceforth autonomous operation for transport purposes within manufacturing/industrial facility without direct human control. In this thesis, new algorithm for state estimation is proposed and analyzed; the algorithm is based on integration of Extended Kalman Filter and feedforward neural networks (Neural Extended Kalman Filter) and camera is used as exteroceptive sensor. Furhermore, to achieve intelligent behavior, the X new robotic hybrid control architecture is developed and analyzed. Finally, the new hybrid control algorithm for guidance of mobile robot is proposed. Two building blocks form the hybrid algorithm: visual servoing and position based control. Neural Extended Kalman Filter is used for state estimation of the mobile robots, and at each time instant the robot knows its position and orientation..

Look-ahead proposals for robust grid-based SLAM with rao-blackwellized particle filters

Simultaneous Localization and Mapping (SLAM) is one of the classical problems in mobile robotics. The task is to build a map of the environment using on-board sensors while at the same time localizing the robot relative to this map. Rao-Blackwellized particle filters have emerged as a powerful technique for solving the SLAM problem in a wide variety of environments. It is a well-known fact for sampling-based approaches that the choice of the proposal distribution greatly influences robustness and efficiency achievable by the algorithm. In this paper, we present an improved proposal distribution for grid-based SLAM with Rao-Blackwellized particle filters, which utilizes whole sequences of sensor measurements rather than only the most recent one. We have implemented our system on a real robot and evaluated its performance on standard datasets as well as in hard outdoor settings with few and ambiguous features. Our approach improves the localization accuracy and the map quality, substantially reducing the risk of mapping failures. © SAGE Publications 2009 Los Angeles, London