Simultaneous Parameter Calibration, Localization, and Mapping

The calibration parameters of a mobile robot play a substantial role in navigation tasks. Often these parameters are subject to variations that depend either on changes in the environment or on the load of the robot. In this paper, we propose an approach to simultaneously estimate a map of the environment, the position of the on-board sensors of the robot, and its kinematic parameters. Our method requires no prior knowledge about the environment and relies only on a rough initial guess of the parameters of the platform. The proposed approach estimates the parameters online and it is able to adapt to non-stationary changes of the configuration. We tested our approach in simulated environments and on a wide range of real-world data using different types of robotic platforms. (C) 2012 Taylor & Francis and The Robotics Society of Japa

Synchronisation et calibrage entre un Lidar 3D et une centrale inertielle pour la localisation précise d'un véhicule autonome

International audienceLaser remote sensing (Lidar) is a technology increasingly used especially in the perception layers of autonomous vehicles. As the vehicle moves during measurement, Lidar data must be referenced in a fixed frame which is usually done thanks to an inertial measurement unit (IMU). However, these sensors are not designed to work together natively thus it is necessary to synchronize and calibrate them carefully. This article presents a method for characterizing timing offsets between a 3D Lidar and an inertial measurement unit. It also explains how to implement the usual methods for pose estimation between an IMU and a Lidar when using such sensors in real conditions.La télédétection par laser (Lidar) est une technologie de plus en plus utilisée en particulier dans les fonctions de perception et localisation nécessaires à la conduite autonome. L'acquisition des données Lidar doit être couplée à la mesure du mouvement du véhicule par une centrale inertielle. Ces capteurs n'étant pas conçus pour fonctionner ensemble nativement, il est nécessaire de maitriser leur synchronisation et leur calibrage géométrique. Cet article présente une méthode pour caractériser les décalages temporels entre un Lidar 3D et une centrale inertielle. Il explique aussi comment mettre en œuvre les méthodes de la littérature pour le calcul de la pose entre centrale inertielle et Lidar sur un véhicule utilisé en conditions réelles

Simultaneous Calibration of Odometry and Camera for a Differential Drive Mobile Robot

Differential-drive mobile robots are usually equipped with video-cameras for navigation purposes. In order to ensure proper operational capabilities of such systems, several calibration steps are required to estimate the following quantities: the video-camera intrinsic and extrinsic parameters, the relative pose between the camera and the vehicle frame and, finally, the odometric parameters of the vehicle. In this paper the simultaneous estimation of the above mentioned quantities is achieved by a systematic and effective calibration procedure that does not require any iterative step. The calibration procedure needs only on-board measurements given by the wheels encoders, the camera and a number of properly taken camera snapshots of a set of known landmarks. Numerical simulations and experimental results with a mobile robot Khepera III equipped with a low-cost camera confirm the effectiveness of the proposed technique