Daniele Marzorati

Rawseeds ground truth collection systems for indoor self-localization and mapping

A trustable and accurate ground truth is a key requirement for benchmarking self-localization and mapping algorithms; on the other hand, collection of ground truth is a complex and daunting task, and its validation is a challenging issue. In this paper we propose two techniques for indoor ground truth collection, developed in the framework of the European project Rawseeds, which are mutually independent and also independent on the sensors onboard the robot. These techniques are based, respectively, on a network of fixed cameras, and on a network of fixed laser scanners. We show how these systems are implemented and deployed, and, most importantly, we evaluate their performance; moreover, we investigate the possible fusion of their outputs.

Monocular SLAM with Inverse Scaling Parametrization

The recent literature has shown that it is possible to solve t he monocular Simultaneous Localization And Mapping using both undelayed features initialization and an Extedend Kalman Filter. The key concept, to achieve this result, was the introduction of a new parametrization calle d Unified Inverse Depth that produces measurements equations with a high degree of linearity and allows an efficient and accurate modeling of uncertai nties. In this paper we present a monocular EKF SLAM filter based on an altern ative parametrization, i.e., the Inverse Scaling Parametrizati on, characterized by a reduced number of parameters, a more linear measurement model, and a better modeling of features uncertainty for both low and high parallax features. Experiments in simulation demonstrate that the use of the Inverse Scaling solution improves the monocular EKF SLAM filter when compared with the Unified Inverse Depth approach, while experiments on real da ta show the system working as well.

On the use of inverse scaling in monocular SLAM

Recent works have shown that it is possible to solve the Simultaneous Localization And Mapping problem using an Extended Kalman Filter and a single perspective camera. The principal drawback of these works is an inaccurate modeling of measurement uncertainties, which therefore causes inconsistencies in the filter estimations. A possible solution to proper uncertainty modeling is the Unified Inverse Depth parametrization. In this paper we propose the Inverse Scaling parametrization that still allows an un-delayed initialization of features, while reducing the number of needed parameters and simplifying the measurement model. This novel approach allows a better uncertainty modeling of both low and high parallax features and reduces the likelihood of inconsistencies. Experiments in simulation demonstrate that the use of the Inverse Scaling solution improves the performance of the monocular EKF SLAM filter when compared with the Unified Inverse Depth approach; experiment on real data confirm the applicability of the idea.

Single and Multi Camera Simultaneous Localization and Mapping Using the Extended Kalman Filter: On the Different Parameterizations for 3D Point Features

Simultaneous Localization and Mapping (SLAM) has received quite a lot of attention in the last decades because of its relevance for many applications centered on a mobile observer, such as service robotics and intelligent transportation systems. This paper focuses on the use of recursive Bayesian filtering, as implemented by the Extendend Kalman Filter (EKF), to face the Visual SLAM problem, i.e., when using data from visual sources. In Monocular SLAM, which uses a single camera as unique source of information, it is not possible to directly estimate the depth of a feature from a single image. To handle the severely non-normal distribution representing such uncertainty, inverse parametrizations were developed, capable to deal with such uncertainty and still relying on Gaussian variables. In the paper, after an introduction to EKF-SLAM, we provide a review of different inverse parametrizations, and we introduce a novel proposal, the Framed Inverse Depth (FID) parametrization, which, in terms of consistency, performs similarly to state of the art Monocular SLAM parametrizations, but at a reduced computational cost. All these parametrizations can be used in a stereo and multi camera setting too. An extensive analysis is presented for both Monocular and stereo SLAM, for a simulated environment widely used in the literature as well as on a widely used real dataset.

Particle-based Sensor Modeling for 3D-Vision SLAM

Self localization and mapping with vision is still an open research field. Since redundancy in the sensing suite is too expensive for consumer-level robots, we base on vision as the main sensing system for SLAM. We approach the problem with 3D data from a trinocular vision system. Past experience shows that problems arise as a consequence of inaccurate modeling of uncertainties; interestingly enough, we found that accuracy in modeling the robot pose uncertainty is much less relevant than for the uncertainty on the sensed data. To overcome the severe limitation of linear and Gaussian approximations, we applied a particle-based description of the inherently non-normal probability density distribution of the sensed data; the aim is to increase the success rate of data association, which we see as the most important problem. The increase in correct data associations reduces the uncertainty in the model and, consequently, in the robot pose, respectively estimated with a hierarchical map decomposition and a six degree of freedom extended Kalman filter. In this paper, we present approaches for particle-based sensor modeling and data association, with a comparative experimental evaluation on real 3D vision data.

On Feature Parameterization for EKF-based Monocular SLAM

Abstract In the last years, the Monocular SLAM problem was widely studied, to allow the simultaneous reconstruction of the environment and the localization of the observer, by using a single camera. As for other SLAM problems, a frequently used feature for the representation of the world, is the 3D point. Differently from other SLAM problems, because of the perspective model of the camera, in Monocular SLAM, features cannot be completely perceived and initialized from a single measurement. To solve this issue, different parameterizations have been proposed in the literature, which try to solve also another problem in Monocular SLAM, i.e., the distortion of the Gaussian uncertainty in depth estimation that takes place because of the nonlinear measurement model. In this paper, we start from recent results in consistency analysis for these parameterizations to propose a novel approach to improve EKF-based Monocular SLAM even further. Our claims are sustained by an extended validation on simulated and real data.