Filtering of Artifacts and Pavement Segmentation from Mobile LiDAR Data

International audienceThis paper presents an automatic method for filtering and segmenting 3D point clouds acquired from mobile LIDAR systems. Our approach exploits 3D information by using range images and several morphological operators. Firstly, a detection of artifacts is carried out in order to filter point clouds. The artifact detection is based on a Top-Hat of hole filling algorithm. Secondly, ground segmentation extracts the contour between pavements and roads. The method uses a quasi-flat zone algorithm and a region adjacency graph representation. Edges are evaluated with the local height difference along the corresponding boundary. Finally, edges with a value compatible with the pavement/road difference (about 14[ cm ] ) are selected. Preliminary results demonstrate the ability of this approach to automatically filter artifacts and segment pavements from 3D data

Real-Time Lane Region Detection Using a Combination of Geometrical and Image Features

Over the past few decades, pavement markings have played a key role in intelligent vehicle applications such as guidance, navigation, and control. However, there are still serious issues facing the problem of lane marking detection. For example, problems include excessive processing time and false detection due to similarities in color and edges between traffic signs (channeling lines, stop lines, crosswalk, arrows, etc.). This paper proposes a strategy to extract the lane marking information taking into consideration its features such as color, edge, and width, as well as the vehicle speed. Firstly, defining the region of interest is a critical task to achieve real-time performance. In this sense, the region of interest is dependent on vehicle speed. Secondly, the lane markings are detected by using a hybrid color-edge feature method along with a probabilistic method, based on distance-color dependence and a hierarchical fitting model. Thirdly, the following lane marking information is extracted: the number of lane markings to both sides of the vehicle, the respective fitting model, and the centroid information of the lane. Using these parameters, the region is computed by using a road geometric model. To evaluate the proposed method, a set of consecutive frames was used in order to validate the performanceOver the past few decades, pavement markings have played a key role in intelligent vehicle applications such as guidance, navigation, and control. However, there are still serious issues facing the problem of lane marking detection. For example, problems include excessive processing time and false detection due to similarities in color and edges between traffic signs (channeling lines, stop lines, crosswalk, arrows, etc.). This paper proposes a strategy to extract the lane marking information taking into consideration its features such as color, edge, and width, as well as the vehicle speed. Firstly, defining the region of interest is a critical task to achieve real-time performance. In this sense, the region of interest is dependent on vehicle speed. Secondly, the lane markings are detected by using a hybrid color-edge feature method along with a probabilistic method, based on distance-color dependence and a hierarchical fitting model. Thirdly, the following lane marking information is extracted: the number of lane markings to both sides of the vehicle, the respective fitting model, and the centroid information of the lane. Using these parameters, the region is computed by using a road geometric model. To evaluate the proposed method, a set of consecutive frames was used in order to validate the performanc

Characterization of Black Spot Zones for Vulnerable Road Users in São Paulo (Brazil) and Rome (Italy)

Non-motorized transportation modes, especially cycling and walking, offer numerous benefits, including improvements in the livability of cities, healthy physical activity, efficient urban transportation systems, less traffic congestion, less noise pollution, clean air, less impact on climate change and decreases in the incidence of diseases related to vehicular emissions. Considering the substantial number of short-distance trips, the time consumed in traffic jams, the higher costs for parking vehicles and restrictions in central business districts, many commuters have found that non-motorized modes of transportation serve as viable and economical transport alternatives. Thus, local governments should encourage and stimulate non-motorized modes of transportation. In return, governments must provide safe conditions for these forms of transportation, and motorized vehicle users must respect and coexist with pedestrians and cyclists, which are the most vulnerable users of the transportation system. Although current trends in sustainable transport aim to encourage and stimulate non-motorized modes of transportation that are socially more efficient than motorized transportation, few to no safety policies have been implemented regarding vulnerable road users (VRU), mainly in large urban centers. Due to the spatial nature of the data used in transport-related studies, geospatial technologies provide a powerful analytical method for studying VRU safety frameworks through the use of spatial analysis. In this article, spatial analysis is used to determine the locations of regions that are characterized by a concentration of traffic accidents (black zones) involving VRU (injuries and casualties) in Sao Paulo, Brazil (developing country), and Rome, Italy (developed country). The black zones are investigated to obtain spatial patterns that can cause multiple accidents. A method based on kernel density estimation (KDE) is used to compare the two cities and show economic, social, cultural, demographic and geographic differences and/or similarities and how these factors are linked to the locations of VRU traffic accidents. Multivariate regression analyses (ordinary least squares (OLS) models and spatial regression models) are performed to investigate spatial correlations, to understand the dynamics of VRU road accidents in Sao Paulo and Rome and to detect factors (variables) that contribute to the occurrences of these events, such as the presence of trip generator hubs (TGH), the number of generated urban trips and demographic data. The adopted methodology presents satisfactory results for identifying and delimiting black spots and establishing a link between VRU traffic accident rates and TGH (hospitals, universities and retail shopping centers) and demographic and transport-related data. Document type: Articl

Bio-Inspired Small Field Perception for Navigation and Localization of MAV's in Cluttered Environments

Insects are capable of agile pursuit of small targets while flying in complex cluttered environments. Additionally, insects are able to discern a moving background from smaller targets by combining their lightweight and fast vision system with efficient algorithms occurring in their neurons. On the other hand, engineering systems lack such capabilities since they either require large sensors, complex computations, or both. Bio-inspired small-field perception mechanisms have the potential to enhance the navigation of small unmanned aircraft systems in cluttered unknown environments. In this dissertation, we propose and investigate three methods to extract information about small-field objects from optic flow. The first method, \textit{flow of flow}, is analogous to processes taking place at the medulla level of the fruit-fly visuomotor system. The two other methods proposed are engineering approaches analogous to the figure-detection sensitive neurons at the lobula. All three methods employed demonstrated effective small-field information extraction from optic flow. The methods extract relative distance and azimuth location to the obstacles from an optic flow model. This optic flow model is based on parameterization of an environment containing small and wide-field obstacles. The three methodologies extract the high spatial frequency content of the optic flow by means of an elementary motion detector, Fourier series, and wavelet transforms, respectively. This extracted signal will contain the information about the small-field obstacles. The three methods were implemented on-board both a ground vehicle and an aerial vehicle to demonstrate and validate obstacle avoidance navigation in cluttered environments. Lastly, a localization framework based on wide field integration of nearness information (inverse of depth) is used for estimating vehicle navigation states in an unknown environment. Simulation of the localization framework demonstrates the ability to navigate to a target position using only nearness information

An adaptive wavelet transformation filtering algorithm for improving road anomaly detection and characterization in vehicular technology

Accelerometers are widely used in modern vehicular technologies to automatically detect and characterize road anomalies such as potholes and bumps. However, measurements from an accelerometer are usually plagued by high noise levels, which typically increase the false alarm and misdetection rates of an anomaly detection system. To address this problem, we have developed in this paper an adaptive threshold estimation technique to filter accelerometer measurements effectively to improve road anomaly detection and characterization in vehicular technologies. Our algorithm decomposes the output signal of an accelerometer into multiple scales using wavelet transformation (WT). Then, it correlates the wavelet coefficients across adjacent scales and classifies them using a newly proposed adaptive threshold technique. Furthermore, our algorithm uses a spatial filter to smoothen further the correlated coefficients before using these coefficients to detect road anomalies. Our algorithm then characterizes the detected road anomalies using two unique features obtained from the filtered wavelet coefficients to differentiate potholes from bumps. The findings from several comparative tests suggest that our algorithm successfully detects and characterizes road anomalies with high levels of accuracy, precision and low false alarm rates as compared to other known methods

Team MIT Urban Challenge Technical Report

This technical report describes Team MITs approach to theDARPA Urban Challenge. We have developed a novel strategy forusing many inexpensive sensors, mounted on the vehicle periphery,and calibrated with a new cross-­modal calibrationtechnique. Lidar, camera, and radar data streams are processedusing an innovative, locally smooth state representation thatprovides robust perception for real­ time autonomous control. Aresilient planning and control architecture has been developedfor driving in traffic, comprised of an innovative combination ofwell­proven algorithms for mission planning, situationalplanning, situational interpretation, and trajectory control. These innovations are being incorporated in two new roboticvehicles equipped for autonomous driving in urban environments,with extensive testing on a DARPA site visit course. Experimentalresults demonstrate all basic navigation and some basic trafficbehaviors, including unoccupied autonomous driving, lanefollowing using pure-­pursuit control and our local frameperception strategy, obstacle avoidance using kino-­dynamic RRTpath planning, U-­turns, and precedence evaluation amongst othercars at intersections using our situational interpreter. We areworking to extend these approaches to advanced navigation andtraffic scenarios

Assessment of Camera Pose Estimation Using Geo-Located Images from Simultaneous Localization and Mapping

This research proposes a method for enabling low-cost camera localization using geo-located images generated with factorgraph-based Simultaneous Localization And Mapping (SLAM). The SLAM results are paired with panoramic image data to generate geo-located images, which can be used to locate and orient low-cost cameras. This study determines the efficacy of using a spherical camera and LIDAR sensor to enable localization for a wide range of cameras with low size, weight, power, and cost. This includes determining the accuracy of SLAM when geo-referencing images, along with introducing a promising method for extracting range measurements from monocular images of known features