François Goulette

Digitizing and 3D modeling of urban environments and roads using vehicle-borne laser scanner system

In this paper we present a system for three-dimensional environment modeling. It consists of an instrumented vehicle equipped with a 2D laser range scanner for data mapping, and GPS, INS and odometers for vehicle positioning and attitude information. The advantage of this system is its ability to perform data acquisition during the vehicle navigation; the sensor needed being a basic 2D scanner with opposition to traditional expensive 3D sensors. This system integrates the laser raw range data with the vehicles internal state estimator and is capable of reconstructing the 3D geometry of the environment by real-time geo-referencing. We propose a high level representation of the urban scene while identifying automatically and in real time some types of existing objects in this environment. Thus, our modeling is articulated around three principal axes: the segmentation, decimation, the 3D reconstruction and visualization. The road is the most important object for us; some road features like the curvature and the width are extracted.

Accurate 3D maps from depth images and motion sensors via nonlinear Kalman filtering

This paper investigates the use of depth images as localisation sensors for 3D map building. The localisation information is derived from the 3D data thanks to the ICP (Iterative Closest Point) algorithm. The covariance of the ICP, and thus of the localization error, is analysed, and described by a Fisher Information Matrix. It is advocated this error can be much reduced if the data is fused with measurements from other motion sensors, or even with prior knowledge on the motion. The data fusion is performed by a recently introduced specific extended Kalman filter, the so-called Invariant EKF, and is directly based on the estimated covariance of the ICP. The resulting filter is natural, and is proved to possess strong properties. Experiments with a Kinect sensor and a three-axis gyroscope prove clear improvement in the accuracy of the localization, and thus in the accuracy of the built 3D map.

Automatic CAD modeling of industrial pipes from range images

We present in this paper a method to obtain automatically CAD models of industrial pipes from range images. The models are based on two geometric primitives, cylinders and torii, which are enough to represent most parts of the pipes. The images are obtained with an accurate long-distance laser range sensor developed for reverse engineering in industrial structures. The key issue for automatic CAD modeling is the automatic segmentation of the data into subsets of points corresponding to the desired primitives. To do so, we use differential geometry to segment lines of centers of curvature into straight and curved parts, corresponding to the cylinder and torus parts of the original image. Differential geometry results being noisy and biased, we use an optimal approach for the computation of centers of curvature.

Registration of fixed-and-mobile- based terrestrial Laser data sets with DSM

This study tackles the production of 3D realistic map databases for outdoor environments. An approach based on the fusion of heterogeneous 3D representations was studied. We propose a variant of ICP (Iterative Closest Point) based on an adaptive dynamic threshold and a RANSAC removing outliers process. An application of our approach was tested on two scenarios: a registration of point clouds obtained by a fixed terrestrial 3D Laser on a 2.5D data Digital Model of Surface (DSM), and the registration of point clouds obtained by a Mobile Mapping System (MMS) on DSM. The objective is to obtain coherence of a homogeneous 3D representation, which will be the input of the following processing step: 3D modeling. We treat various scenarios of registration which were specified; the algorithm exploits specific pre-processing dedicated to the studied use-cases.

A robust linear feature-based procedure for automated registration of point clouds.

With the variety of measurement techniques available on the market today, fusing multi-source complementary information into one dataset is a matter of great interest. Target-based, point-based and feature-based methods are some of the approaches used to place data in a common reference frame by estimating its corresponding transformation parameters. This paper proposes a new linear feature-based method to perform accurate registration of point clouds, either in 2D or 3D. A two-step fast algorithm called Robust Line Matching and Registration (RLMR), which combines coarse and fine registration, was developed. The initial estimate is found from a triplet of conjugate line pairs, selected by a RANSAC algorithm. Then, this transformation is refined using an iterative optimization algorithm. Conjugates of linear features are identified with respect to a similarity metric representing a line-to-line distance. The efficiency and robustness to noise of the proposed method are evaluated and discussed. The algorithm is valid and ensures valuable results when pre-aligned point clouds with the same scale are used. The studies show that the matching accuracy is at least 99.5%. The transformation parameters are also estimated correctly. The error in rotation is better than 2.8% full scale, while the translation error is less than 12.7%.

A new method and a clinical case for computer assisted dental implantology

This paper presents a new method for dental implant surgery. A pre-operative planning software is used to work with CT scanner data. Implant fixtures are placed with the help of a 3D reconstructed model of the patient’s jaw. An accurate robot is then used to drill a jaw splint, at the locations determined with the planning software, in order to make a surgical guide. The matching between image and robot referentials is performed with radio-opaque markers attached in a specific way to the jaw splint. A clinical case of this new technique is presented.

Computer Assisted Dental Implantology: A New Method and a Clinical Validation

This paper presents a new method for dental implant surgery. A preoperative planning software is used to work with CT scanner data. Implant fixtures are placed with the help of a 3D reconstructed model of the patients jaw. An accurate robot is then used to drill a jaw splint, at the locations determined with the planning software, in order to make a surgical guide. A validation case of this new technique is also presented.

Experimental implementation of an Invariant Extended Kalman Filter-based scan matching SLAM

We describe an application of the Invariant Extended Kalman Filter (IEKF) design methodology to the scan matching SLAM problem. We review the theoretical foundations of the IEKF and its practical interest of guaranteeing robustness to poor state estimates, then implement the filter on a wheeled robot hardware platform. The proposed design is successfully validated in experimental testing.

Onboard real-time system for 3D urban environment reconstruction

The on-board real-time system for three-dimensional environment reconstruction consists of: an instrumented vehicle equipped by a laser mapping system using laser scanner for three dimensional range data acquisition, GPS, INS and odometers for positioning and attitude information. The data obtained by this system could be good resources for developing urban 3D database, which has numerous applications in the field of car navigation and 3D map matching, virtual reality, video and computer games, and planning. The systems on-board software platform /sup RT/MAPS integrates the laser raw range data with the developed vehicle state estimator and is capable to reconstruct the 3D geometry of the environment by real time geo-referencing.

Automatic Ground Surface Reconstruction from mobile laser systems for driving simulation engines

Driving simulation engines represent a cost effective solution for vehicle development, being employed for performing feasibility studies and tests failure and for assessing new functionalities. Nevertheless, they require geometrically accurate and realistic three-dimensional (3D) models in order to allow driver training. This paper presents the Automatic Ground Surface Reconstruction method, a framework that exploits 3D data acquired by Mobile Laser Scanning systems. They are particularly attractive due to their fast acquisition at the terrestrial level. Nevertheless, such a mobile acquisition introduces several constraints for the existing 3D surface reconstruction algorithms. The proposed surface modeling framework produces a regular surface and recovers sharp depth features within a scalable and detail-preserving framework. Experimental results on real data acquired in urban environments allow us to conclude on the effectiveness of the proposed method.