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

Robust optimization of factor graphs by using condensed measurements

Popular problems in robotics and computer vision like simultaneous localization and mapping (SLAM) or structure from motion (SfM) require to solve a least-squares problem that can be effectively represented by factor graphs. The chance to find the global minimum of such problems depends on both the initial guess and the non-linearity of the sensor models. In this paper we propose an approach to determine an approximation of the original problem that has a larger convergence basin. To this end, we employ a divide-and-conquer approach that exploits the structure of the factor graph. Our approach has been validated on real-world and simulated experiments and is able to succeed in finding the global minimum in situations where other state-of-the-art methods fail

Range sensor based model construction by sparse surface adjustment

In this paper, we propose an approach to construct highly accurate 3D object models from range data. The main advantage of sensor based model acquisition compared to manual CAD model construction is the short time needed per object. The usual drawbacks of sensor based model reconstruction are sensor noise and errors in the sensor positions which typically lead to less accurate models. Our method drastically reduces this problem by applying a physical model of the underlying range sensor and utilizing a graph-based optimization technique. We present our approach and evaluate it on data recorded in different real world environments with an RGBD camera and a laser range scanner. The experimental results demonstrate that our method provides more accurate maps than standard SLAM methods and that it additionally compares favorable over the moving least squares method. © 2011 IEEE

Simultaneous 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 environmental changes or on the wear of the devices. 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 platform parameters. The proposed approach performs on-line estimation of the parameters 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. © 2011 IEEE

Highly accurate 3D surface models by sparse surface adjustment

In this paper, we propose an approach to obtain highly accurate 3D models from range data. The key idea of our method is to jointly optimize the poses of the sensor and the positions of the surface points measured with a range scanning device. Our approach applies a physical model of the underlying range sensor. To solve the optimization task it employs a state-of-the-art graph-based optimizer and iteratively refines the structure of the error function by recomputing the data associations after each optimization. We present our approach and evaluate it on data recorded in different real world environments with a RGBD camera and a laser range scanner. The experimental results demonstrate that our method is able to substantially improve the accuracy of SLAM results and that it compares favorable over the moving least squares method. © 2012 IEEE

Highly accurate maximum likelihood laser mapping by jointly optimizing laser points and robot poses

In this paper we describe an algorithm for learning highly accurate laser-based maps that treats the overall mapping problem as a joint optimization problem over robot poses and laser points. We assume that a laser range finder senses points sampled from a regular surface and we utilize an improved likelihood function that accounts for two phenomena affecting the laser measurements that are often neglected: the conic shape of the laser beam and the incidence angle. To solve the entire problem we apply an optimization procedure that jointly adjusts the position of all the robot poses and all points in the scans. As a result, we obtain highly accurate maps. We evaluated our approach using simulated and real-world data and we show that utilizing the estimated maps greatly improves the localization accuracy of robots. The results furthermore suggest that the accuracy of the resulting map can exceed the resolution of the laser sensors used. © 2011 IEEE

G2o: A general framework for graph optimization

Many popular problems in robotics and computer vision including various types of simultaneous localization and mapping (SLAM) or bundle adjustment (BA) can be phrased as least squares optimization of an error function that can be represented by a graph. This paper describes the general structure of such problems and presents g2o, an open-source C++ framework for optimizing graph-based nonlinear error functions. Our system has been designed to be easily extensible to a wide range of problems and a new problem typically can be specified in a few lines of code. The current implementation provides solutions to several variants of SLAM and BA. We provide evaluations on a wide range of real-world and simulated datasets. The results demonstrate that while being general g2o offers a performance comparable to implementations of state-of-the-art approaches for the specific problems. © 2011 IEEE