NICP: Dense normal based point cloud registration

In this paper we present a novel on-line method to recursively align point clouds. By considering each point together with the local features of the surface (normal and curvature), our method takes advantage of the 3D structure around the points for the determination of the data association between two clouds. The algorithm relies on a least squares formulation of the alignment problem, that minimizes an error metric depending on these surface characteristics. We named the approach Normal Iterative Closest Point (NICP in short). Extensive experiments on publicly available benchmark data show that NICP outperforms other state-of-the-art approaches

Fast and robust 3D feature extraction from sparse point clouds

Matching 3D point clouds, a critical operation in map building and localization, is difficult with Velodyne-type sensors due to the sparse and non-uniform point clouds that they produce. Standard methods from dense 3D point clouds are generally not effective. In this paper, we describe a featurebased approach using Principal Components Analysis (PCA) of neighborhoods of points, which results in mathematically principled line and plane features. The key contribution in this work is to show how this type of feature extraction can be done efficiently and robustly even on non-uniformly sampled point clouds. The resulting detector runs in real-time and can be easily tuned to have a low false positive rate, simplifying data association. We evaluate the performance of our algorithm on an autonomous car at the MCity Test Facility using a Velodyne HDL-32E, and we compare our results against the state-of-theart NARF keypoint detector. © 2016 IEEE

FLAT2D: Fast localization from approximate transformation into 2D

Many autonomous vehicles require precise localization into a prior map in order to support planning and to leverage semantic information within those maps (e.g. that the right lane is a turn-only lane.) A popular approach in automotive systems is to use infrared intensity maps of the ground surface to localize, making them susceptible to failures when the surface is obscured by snow or when the road is repainted. An emerging alternative is to localize based on the 3D structure around the vehicle; these methods are robust to these types of changes, but the maps are costly both in terms of storage and the computational cost of matching. In this paper, we propose a fast method for localizing based on 3D structure around the vehicle using a 2D representation. This representation retains many of the advantages of "full" matching in 3D, but comes with dramatically lower space and computational requirements. We also introduce a variation of Graph-SLAM tailored to support localization, allowing us to make use of graph-based error-recovery techniques in our localization estimate. Finally, we present real-world localization results for both an indoor mobile robotic platform and an autonomous golf cart, demonstrating that autonomous vehicles do not need full 3D matching to accurately localize in the environment

A Proposal for Semantic Map Representation and Evaluation

Semantic mapping is the incremental process of “mapping” relevant information of the world (i.e., spatial information, temporal events, agents and actions) to a formal description supported by a reasoning engine. Current research focuses on learning the semantic of environments based on their spatial location, geometry and appearance. Many methods to tackle this problem have been proposed, but the lack of a uniform representation, as well as standard benchmarking suites, prevents their direct comparison. In this paper, we propose a standardization in the representation of semantic maps, by defining an easily extensible formalism to be used on top of metric maps of the environments. Based on this, we describe the procedure to build a dataset (based on real sensor data) for benchmarking semantic mapping techniques, also hypothesizing some possible evaluation metrics. Nevertheless, by providing a tool for the construction of a semantic map ground truth, we aim at the contribution of the scientific community in acquiring data for populating the dataset

Robots for Exploration, Digital Preservation and Visualization of Archeological Sites

Monitoring and conservation of archaeological sites are important activities necessary to prevent damage or to perform restoration on cultural heritage. Standard techniques, like mapping and digitizing, are typically used to document the status of such sites. While these task are normally accomplished manually by humans, this is not possible when dealing with hard-to-access areas. For example, due to the possibility of structural collapses, underground tunnels like catacombs are considered highly unstable environments. Moreover, they are full of radioactive gas radon that limits the presence of people only for few minutes. The progress recently made in the artificial intelligence and robotics field opened new possibilities for mobile robots to be used in locations where humans are not allowed to enter. The ROVINA project aims at developing autonomous mobile robots to make faster, cheaper and safer the monitoring of archaeological sites. ROVINA will be evaluated on the catacombs of Priscilla (in Rome) and S. Gennaro (in Naples)

Using extended measurements and scene merging for efficient and robust point cloud registration

Point cloud registration is a fundamental building block of many robotic applications. In this paper we describe a system to solve the registration problem, that builds on top of our previous work (Serafin and Grisetti (2015)), and that represents an extension to the well known Iterative Closest Point (ICP) algorithm. Our approach combines recent achievements on optimization by using an extended point representation (Serafin and Grisetti (2014)) that captures the surface characteristics around the points. Thanks to an effective strategy to search for correspondences, our method can operate on-line and cope with measurements gathered with an heterogeneous set of range and depth sensors. By using an efficient map-merging procedure our approach can quickly update the tracked scene and handle dynamic aspects. We also introduce an approximated variant of our method that runs at twice the speed of our full implementation. Experiments performed on a large publicly available benchmarking dataset show that our approach performs better with respect to other state-of-the art methods. In most of the tests considered, our algorithm has been able to obtain a translational and rotational relative error of respectively cm and 1 de

Using augmented measurements to improve the convergence of icp

Point cloud registration is an essential part for many robotics applications and this problem is usually addressed using some of the existing variants of the Iterative Closest Point (ICP) algorithm. In this paper we propose a novel variant of the ICP objective function which is minimized while searching for the registration. We show how this new function, which relies not only on the point distance, but also on the difference between surface normals or surface tangents, improves the registration process. Experiments are performed on synthetic data and real standard benchmark datasets, showing that our approach outperforms other state of the art techniques in terms of convergence speed and robustness

Efficient Traversability Analysis for Mobile Robots using the Kinect Sensor

Abstract — For autonomous robots, the ability to classify their local surroundings into traversable and non-traversable areas is crucial for navigation. In this paper, we address the problem of online traversability analysis for robots that are only equipped with a Kinect-style sensor. Our approach processes the depth data at 10 fps-25 fps on a standard notebook computer without using the GPU and allows for robustly identifying the areas in front of the sensor that are safe for navigation. The component presented here is one of the building blocks of the EU project ROVINA that aims at the exploration and digital preservation of hazardous archeological sites with mobile robots. Real world evaluations have been conducted in controlled lab environments, in an outdoor scene, as well as in a real, partially unexplored, and roughly 1700 year old Roman catacomb. I

Efficient traversability analysis for mobile robots using the Kinect sensor

For autonomous robots, the ability to classify their local surroundings into traversable and non-traversable areas is crucial for navigation. In this paper, we address the problem of online traversability analysis for robots that are only equipped with a Kinect-style sensor. Our approach processes the depth data at 10 fps-25 fps on a standard notebook computer without using the GPU and allows for robustly identifying the areas in front of the sensor that are safe for navigation. The component presented here is one of the building blocks of the EU project ROVINA that aims at the exploration and digital preservation of hazardous archeological sites with mobile robots. Real world evaluations have been conducted in controlled lab environments, in an outdoor scene, as well as in a real, partially unexplored, and roughly 1700 year old Roman catacomb. © 2013 IEEE

Do Grade II Ankle Sprains Have Chronic Effects on the Functional Ability of Ballet Dancers Performing Single-Leg Flat-Foot Stance? An Observational Cross-Sectional Study

Ballet dancers have a higher risk than the general population of ankle sprains. Ankle proprioception is of the utmost importance for executing static and dynamic positions typical of ballet dancing. Ankle sprains can create changes in functional ability that may affect ballet performance. The aim of this cross-sectional observational study is to evaluate if non-professional ballet dancers that were previously injured with a grade II ankle sprain carry a long-term stability deficit in ballet specific positions (passé, arabesque) and in single-leg flat-foot stance, thereby affecting ballet performance. We enrolled 22 amateur female ballet dancers, 11 who previously had a grade II ankle injury and 11 who had no history of ankle injury. Stabilometric data (Center of Pressure Speed and Elipse Area) were assessed with the postural electronic multisensory baropodometer in normal, arabesque, and passè positions with both open and closed eyes. Using an unpaired t-test, we compared healthy and pathological feet of the ankle injury group for a standard monopodalic position and two ballet-specific positions. No difference between pathological and healthy feet of non-professional ballet dancers who suffered grade II ankle injury was detected. According to the parameters considered in this study, grade II ankle sprains seem to have a favorable prognosis in the sample that we evaluated