André Monin

Undelayed initialization in bearing only SLAM

Most solutions to the SLAM problem in robotics have utilised range and beating sensors as the provided perception data is easy to incorporate, allowing immediate landmark initialization. This is not the case when using bearing-only information because the distance to the perceived landmarks is not directly provided. A whole estimate of a landmark position is only possible via a set of measurements taken from different points of view. The vast majority of contributions to this problem perform a parallel task to get this estimate, and hence the landmark initialization is delayed. We give a new insight to the problem and present a method to avoid this delay by initializing the whole ray that defines the direction of the landmark. We utilize a minimal and computationally efficient form to represent this ray and a new strategy for the subsequent updates. Simulations have been carried out to validate the proposed algorithms.

On human motion imitation by humanoid robot

In this paper, the imitation of human captured motions by a humanoid robot is considered. The main objective is to reproduce an imitated motion which should be as close as possible to the original human captured motion. To achieve this goal, the imitation problem is formulated as an optimization problem and the physical limits of the humanoid robot are considered as constraints. The optimization problem is then solved recursively by using an efficient dynamics algorithm, which allows the calculation of the gradient function with respect to the control parameters analytically. The simulation results using OpenHRP platform, which is a dynamical simulator for humanoid robot motions, have pointed out that the imitated motions preserve the salient characteristics of the original human captured motion. Moreover the optimization procedure converges well thanks to the analytical calculation of the gradient function.

Fusing Monocular Information in Multicamera SLAM

This paper explores the possibilities of using monocular simultaneous localization and mapping (SLAM) algorithms in systems with more than one camera. The idea is to combine in a single system the advantages of both monocular vision (bearings-only, infinite range observations but no 3-D instantaneous information) and stereovision (3-D information up to a limited range). Such a system should be able to instantaneously map nearby objects while still considering the bearing information provided by the observation of remote ones. We do this by considering each camera as an independent sensor rather than the entire set as a monolithic supersensor. The visual data are treated by monocular methods and fused by the SLAM filter. Several advantages naturally arise as interesting possibilities, such as the desynchronization of the firing of the sensors, the use of several unequal cameras, self-calibration, and cooperative SLAM with several independently moving cameras. We validate the approach with two different applications: a stereovision SLAM system with automatic self-calibration of the rigs main extrinsic parameters and a cooperative SLAM system with two independent free-moving cameras in an outdoor setting.

Numerical calibration for 3-axis accelerometers and magnetometers

Magnetometers and accelerometers are sensors that are now integrated in objects of everyday life like automotive applications, mobile phones and so on. Some applications need information of acceleration and attitude with a high accuracy. For example, MEMS magnetometers and accelerometers can be integrated in embedded like mobile phones and GPS receivers. The parameters of such sensors must be precisely estimated to avoid drift and biased values. Thus, calibration is an important step to correctly use these sensors and get the expected measurements. This paper presents the theoretical and experimental steps of a method to compute gains, bias and non orthogonality factors of magnetometer and accelerometer sensors. This method of calibration can be used for automatic calibration in embedded systems. The calibration procedure involves arbitrary rotations of the sensors platform and a visual 2D projection of measurements.

On humanoid motion optimization

In this paper, we present a recursive method for the optimization of humanoid robot motions. The method is based on an efficient dynamics algorithm, which allows the calculation of the gradient function with respect to the control parameters analytically. The algorithm makes use of the theory of Lie groups and Lie algebra. The main objective of this method is to smooth the pre-calculated humanoid motions by minimizing the efforts, and at the same time improving the stability of the humanoid robot during the execution of the planned tasks. Experimental results using HRP-2 platform are provided to validate the proposed method.

BiCamSLAM: Two times mono is more than stereo

This paper is an invitation to use mono-vision techniques on stereo-vision equipped robots. By using monocular algorithms on both cameras, the advantages of mono-vision (bearing-only, with infinity range but no 3D instant information) and stereo-vision (3D information only up to a limited range) naturally add up to provide interesting possibilities, that are here developed and demonstrated using an EKF-based monocular SLAM algorithm. Mainly we obtain: a) fast 3D mapping with long term, absolute angular references; b) great landmark updating flexibility; and c) the possibility of stereo rig extrinsic self-calibration, providing a much more robust and accurate sensor. Experimental results show the pertinence of the proposed ideas, which should be easily exportable (and we encourage to do so) to other, more performing, vision-based SLAM algorithms.

I.I.R. Volterra Filtering with Application to Bilinear Systems

A new nonlinear filtering technique by means of infinite impulse response (IIR) Volterra functionals is developed. It yields the projection onto the closed class of finite Volterra series with separable kernels of arbitrary degree k. Such an optimal estimator is finitely realizable as a bilinear system with parameters that are computable off line. Moreover, if the original system model is itself bilinear, this computation is finitely recursive through higher moments of degree 2 k. Two simple illustrating examples are developed: (i) estimation of the covariance of the internal white noise driving a linear system and (ii) filtering of a non-Gaussian linear system (driven by a Poisson process). The robustness with respect to the observation noise distribution is finally examined.

Time Parameterization of Humanoid-Robot Paths

This paper proposes a unified optimization framework to solve the time-parameterization problem of humanoid-robot paths. Even though the time-parameterization problem is well known in robotics, the application to humanoid robots has not been addressed. This is because of the complexity of the kinematical structure as well as the dynamical motion equation. The main contribution of this paper is to show that the time parameterization of a statically stable path to be transformed into a dynamically stable trajectory within the humanoid-robot capacities can be expressed as an optimization problem. Furthermore, we propose an efficient method to solve the obtained optimization problem. The proposed method has been successfully validated on the humanoid robot HRP-2 by conducting several experiments. These results have revealed the effectiveness and the robustness of the proposed method.

Minimum variance estimation of parameters constrained by bounds

This correspondence deals with an extension of minimum variance estimation when the parameter to be estimated is constrained by bounds. It is shown that a particular initial distribution allows finite-dimensional calculation and leads to a nonlinear filter. More precisely, it is shown that a truncated Gaussian distribution is preserved a long time, leading to a finite number of parameters to be computed. Proof of the main theorem is straightforward with significant application such as positive real amplitude estimation. Performance gains are shown on the LORAN-C signal reception example.

Lie algebraic canonical representations in nonlinear control systems

This paper is the applied counterpart to previous results [5] for linear-analytic control systems. It is mainly concerned with two canonical representations of the exponential type. They exhibit the Lie algebraic structure of the system in such a form that results on weak controllability are easily derived in an algebraic manner. The first representation is a single exponential of a canonical Lie series in Halls basis of the Lie algebra of vector fields. The second one is a factorization in terms of simpler exponentials of Halls basic vectors. Both of them exhibit, as canonical coefficients, an infinite set of characteristic parameters which are a minimal representation of the input paths, when no drift occurs in the system (or, equivalently, in the weak control case). The weak controllability theorem is easily derived from these results, in a purely algebraic way.