On the Covariance of ICP-based Scan-matching Techniques

This paper considers the problem of estimating the covariance of roto-translations computed by the Iterative Closest Point (ICP) algorithm. The problem is relevant for localization of mobile robots and vehicles equipped with depth-sensing cameras (e.g., Kinect) or Lidar (e.g., Velodyne). The closed-form formulas for covariance proposed in previous literature generally build upon the fact that the solution to ICP is obtained by minimizing a linear least-squares problem. In this paper, we show this approach needs caution because the rematching step of the algorithm is not explicitly accounted for, and applying it to the point-to-point version of ICP leads to completely erroneous covariances. We then provide a formal mathematical proof why the approach is valid in the point-to-plane version of ICP, which validates the intuition and experimental results of practitioners.Comment: Accepted at 2016 American Control Conferenc

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 very 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.Comment: Submitted to IROS 2012. 8 page

Co-registration of DSM and 3D points clouds acquired by a Mobile Mapping System

International audienceThe development of 3D mapping databases is a matter of increasing interest. Databases have recently been developed at different scales (national, European, international) and to meet different needs. Such development has been made possible by the implementation of efficient 3D mapping technologies. 3D mapping strategies are based on multi-sensor data fusion usually performed after a preprocessing step that includes registration and filtering. In this paper, we present our work on registration methods applied to solve problems in 3D urban environment representations issued from a Mobile Mapping System. We improve the stability of convergence, the computation time and handle heterogeneous data sets in various scenarios

COREGISTRATION OF DSM AND 3D POINT CLOUDS ACQUIRED BY A MOBILE MAPPING SYSTEM

The development of 3D mapping databases is a matter of increasing interest. Databases have recently been developed at different scales (national, European, international) and to meet different needs. Such development has been made possible by the implementation of efficient 3D mapping technologies. 3D mapping strategies are based on multi-sensor data fusion usually performed after a preprocessing step that includes registration and filtering. In this paper, we present our work on registration methods applied to solve problems in 3D urban environment representations issued from a Mobile Mapping System. We improve the stability of convergence, the computation time and handle heterogeneous data sets in various scenarios

Invariant EKF Design for Scan Matching-aided Localization

Localization in indoor environments is a technique which estimates the robot's pose by fusing data from onboard motion sensors with readings of the environment, in our case obtained by scan matching point clouds captured by a low-cost Kinect depth camera. We develop both an Invariant Extended Kalman Filter (IEKF)-based and a Multiplicative Extended Kalman Filter (MEKF)-based solution to this problem. The two designs are successfully validated in experiments and demonstrate the advantage of the IEKF design

Assessing the Accuracy of Land-Based Mobile Laser Scanning Data

Also available online at http://journals.bg.agh.edu.pl/GEOMATICS/2012.6.3/geom.2012.6.3.73.pdfInternational audienceIn this paper, we have described an accuracy analysis of MLS point clouds collected using the LARA3D prototype platform in an urban area. Accuracy of the MLS was achieved through comparison with other data sources more accurate that the studied system. The study has shown, that when compared with control points, collected by a Total Station, the prototype system LARA3D is able to produce data with an accuracy better then 0.3 m. However, taking into consideration the uncertainty in the identification of common points, this method is affected by man-made error and limited by point cloud resolutions. Meanwhile, the use of existing reference data, such as e.g. high resolution point clouds from static terrestrial laser scanning provides fast and reliable data evaluation. The subjective element of operator interpretation is also removed. Results achieved using ICP algorithm show, that our mobile mapping system suffers from limitations of the sensor quality and Kalman filter implementation. In the case of point clouds locally degraded, proper matching is impossible and the obtained result does not reflect the type and scale of deformation correctly. Meanwhile, another less time-consuming and more automated method for assessing data accuracy should be developed. That may be referred to using the existing spatial data as reference, such as e.g.: cadastre, ALS data, Topographic Data Base (TBD), Digital Terrain Model, orthophotos and so on

Fast computation of soft tissue deformations in real-time simulation with Hyper-Elastic Mass Links

International audienceVirtual surgery simulators show a lot of advantages in the world of surgery training, where they allow to improve the quality of surgeons' gesture. One of the current major technical difficulties for the development of surgery simulation is the possibility to perform a real-time computation of soft tissue deformation by considering the accurate modeling of their mechanical properties. However today, few models are available, they are still time consuming and limited in number of elements by algorithm complexity. We present in this paper a new method and framework that we call 'HEML' (Hyper-Elastic Mass Links), which is particularly fast. It is derived from the finite element method, can handle visco-hyperelastic and large deformation modeling. Although developed initially for medical applications, the HEML method can be used for any numerical computation of hyperelastic material deformations based on a tetrahedral mesh. A comparison with existing methods shows a much faster speed. A comparison with Mass-Spring methods, that are particularly fast but not realistic, shows that they can be considered as a degenerate case of the HEML framework

MDT3D: Multi-Dataset Training for LiDAR 3D Object Detection Generalization

Supervised 3D Object Detection models have been displaying increasingly better performance in single-domain cases where the training data comes from the same environment and sensor as the testing data. However, in real-world scenarios data from the target domain may not be available for finetuning or for domain adaptation methods. Indeed, 3D object detection models trained on a source dataset with a specific point distribution have shown difficulties in generalizing to unseen datasets. Therefore, we decided to leverage the information available from several annotated source datasets with our Multi-Dataset Training for 3D Object Detection (MDT3D) method to increase the robustness of 3D object detection models when tested in a new environment with a different sensor configuration. To tackle the labelling gap between datasets, we used a new label mapping based on coarse labels. Furthermore, we show how we managed the mix of datasets during training and finally introduce a new cross-dataset augmentation method: cross-dataset object injection. We demonstrate that this training paradigm shows improvements for different types of 3D object detection models. The source code and additional results for this research project will be publicly available on GitHub for interested parties to access and utilize: https://github.com/LouisSF/MDT3DComment: Accepted for publication at IROS 202

Paris-rue-Madame database: a 3D mobile laser scanner dataset for benchmarking urban detection, segmentation and classification methods

International audienceThis paper describes a publicly available 3D database from the rue Madame, a street in the 6th Parisian district. Data have been acquired by the Mobile Laser Scanning (MLS) system L3D2 and correspond to a 160 m long street section. Annotation has been carried out in a manually assisted way. An initial annotation is obtained using an automatic segmentation algorithm. Then, a manual refinement is done and a label is assigned to each segmented object. Finally, a class is also manually assigned to each object. Available classes include facades, ground, cars, motorcycles, pedestrians, traffic signs, among others. The result is a list of (X, Y, Z, reflectance, label, class) points. Our aim is to offer, to the scientific community, a 3D manually labeled dataset for detection, segmentation and classification benchmarking. With respect to other databases available in the state of the art, this dataset has been exhaustively annotated in order to include all available objects and to allow point-wise comparison

SIMULATION BASED COMPARATIVE ANALYSIS FOR THE DESIGN OF LASER TERRESTRIAL MOBILE MAPPING SYSTEMS

Over the past decade, laser terrestrial Mobile Mapping Systems (MMS) have been developed for the digital mapping of outdoor environments. While the applications of MMS are various (urban security, road control, virtual world, entertainment, etc.), one may imagine that for each application the system designs could be different. Hence, a comparative analysis of different designs may be useful to find the best solution adapted to each application. The objective of this paper is to propose a methodology to compare point-cloud data quality from different MMS designs by modifying spatial configuration of laser imaging system. For this methodology, we define several quality criteria such as precision, resolution, completeness. We illustrate this in the case of urban architecture digital mapping based on the use of a simulator