1,720,984 research outputs found
Pavement Distress Detection and Avoidance for Intelligent Vehicles
No full textPavement distresses and potholes represent road hazards that can cause accidents and damages to vehicles. The latter may vary from a simple flat tyre to serious failures of the suspension system, and in extreme cases to collisions with third-party vehicles and even endanger passengers' lives. The primary scientific aim of this study is to investigate the problem of road hazard detection for driving assistance purposes, towards the final goal of implementing such a technology on future intelligent vehicles. The proposed approach uses a depth sensor to generate an environment representation in terms of 3D point cloud that is then processed by a normal vector-based analysis and presented to the driver in the form of a traversability grid. Even small irregularities of the road surface can be successfully detected. This information can be used either to implement driver warning systems or to generate, using a cost-to-go planning method, optimal trajectories towards safe regions of the carriageway. The effectiveness of this approach is demonstrated on real road data acquired during an experimental campaign. Normal analysis and path generation are performed in post-analysis. This approach has been demonstrated to be promising and may help to drastically reduce fatal traffic casualties, as a high percentage of road accidents are related to pavement distress
3D traversability awareness for rough terrain mobile robots
No full textThis research aims to address the issue of safe navigation for autonomous vehicles in highly challenging outdoor environments. Indeed, robust navigation of autonomous mobile robots over long distances requires advanced perception means for terrain traversability assessment. Design/methodology/ approach - The use of visual systems may represent an efficient solution. This paper discusses recent findings in terrain traversability analysis from RGB-D images. In this context, the concept of point as described only by its Cartesian coordinates is reinterpreted in terms of local description. As a result, a novel descriptor for inferring the traversability of a terrain through its 3D representation, referred to as the unevenness point descriptor (UPD), is conceived. This descriptor features robustness and simplicity. Findings - The UPD-based algorithm shows robust terrain perception capabilities in both indoor and outdoor environment. The algorithm is able to detect obstacles and terrain irregularities. The system performance is validated in field experiments in both indoor and outdoor environments. Research limitations/implications - The UPD enhances the interpretation of 3D scene to improve the ambient awareness of unmanned vehicles. The larger implications of this method reside in its applicability for path planning purposes. Originality/value - This paper describes a visual algorithm for traversability assessment based on normal vectors analysis. The algorithm is simple and efficient providing fast real-time implementation, since the UPD does not require any data processing or previously generated digital elevation map to classify the scene. Moreover, it defines a local descriptor, which can be of general value for segmentation purposes of 3D point clouds and allows the underlining geometric pattern associated with each single 3D point to be fully captured and difficult scenarios to be correctly handle
Real-Time Multi-Modal Active Vision for Object Detection on UAVs Equipped With Limited Field of View LiDAR and Camera
No full textThis letter aims to solve the challenging problems in multi-modal active vision for object detection on unmanned aerial vehicles (UAVs) with a monocular camera and a limited Field of View (FoV) LiDAR. The point cloud acquired from the low-cost LiDAR is firstly converted into a 3-channel tensor via motion compensation, accumulation, projection, and up-sampling processes. The generated 3-channel point cloud tensor and RGB image are fused into a 6-channel tensor using an early fusion strategy for object detection based on a Gaussian YOLO network structure. To solve the low computational resource problem and improve the real-time performance, the velocity information of the UAV is further fused with the detection results based on an extended Kalman Filter (EKF). A perception-aware model predictive control (MPC) is designed to achieve active vision on our UAV. According to our performance evaluation, our pre-processing step improves other literature methods running time by a factor of 10 while maintaining acceptable detection performance. Furthermore, our fusion architecture reaches 94.6 mAP on the test set, outperforming the individual sensor networks by roughly 5%. We also described an implementation of the overall algorithm on a UAV platform and validated it in real-world experiments
A new approach for terrain analysis in mobile robot applications
No full textThis paper presents a novel approach to detect traversable and non-traversable regions of the environment from a depth image that could enhance mobility and safety of mobile robots through integration with localization, control and planning methods. The proposed system is based on Principal Component Analysis (PCA). PCA theory provides a powerful means to analyze 3D surfaces widely used in computer vision. It can be successfully applied, as well, to increase the degree of perception in autonomous vehicles, as new generations of 3D imaging sensors, including stereo and RGB-D-cameras, are increasingly introduced. The approach described in this paper is based on the estimation of the normal vector to a local surface leading to the definition of a novel, so-called, Unevenness Point Descriptor. Experimental results, obtained from indoor and outdoor environments, are presented to validate the system. It is demonstrated that the proposed approach can be effectively used for scene segmentation and it can efficiently handle difficult scenarios, including the presence of terrain slopes
Learning Traversability from Point Clouds in Challenging Scenarios
No full textThis paper aims at evaluating the capabilities to detect road traversability in urban and extra-urban scenarios ofsupport vector machine-based classifiers that use local descriptors extracted from point cloud data. The evaluation of the proposed classifiers is carried out by using four different kernels and comparing five point descriptors obtained from geometric and appearance-based features. A comparison among the performance of descriptors individually has demonstrated that the normal vector-based descriptor achieves an accuracy of 88%, outperforming by about 6%–15% all the other considered ones. To further improve the interpretation capabilities, the space of features is augmented by merging the components of each point descriptor, reaching 92% classification accuracy. A set of test scenarios have been acquired during an extensive experimental campaign using an all-terrain vehicle. Tests on real data show high classification performance for road scenarios and rural environments; the generality of the method makes it applicable for different types of mobile robots including, but not limited to, autonomous vehicles
Laser based driving assistance for smart robotic wheelchairs
No full textThis paper is presenting the ongoing work toward a novel driving assistance system of a robotic wheelchair, for people paralyzed from down the neck. The user's head posture is tracked, to accordingly project a colored spot on the ground ahead, with a pan-tilt mounted laser. The laser dot on the ground represents a potential close range destination the operator wants to reach autonomously. The wheelchair is equipped with a low cost depth-camera (Kinect sensor) that models a traversability map in order to define if the designated destination is reachable or not by the chair. If reachable, the red laser dot turns green, and the operator can validate the wheelchair destination via an Electromyogram (EMG) device, detecting a specific group of muscle's contraction. This validating action triggers the calculation of a path toward the laser pointed target, based on the traversability map. The wheelchair is then controlled to follow this path autonomously. In the future, the stream of 3D point cloud acquired during the process will be used to map and self localize the wheelchair in the environment, to be able to correct the estimate of the pose derived from the wheel's encoders
Three Different Approaches for Localization in a Corridor Environment by Means of an Ultrasonic Wide Beam
No full textIn this paper the authors present three methods to detect the position and orientation of an observer, such as a mobile robot, with respect to a corridor wall. They use an inexpensive sensor to spread a wide ultrasonic beam. The sensor is rotated by means of an accurate servomotor in order to propagate ultrasonic waves towards a regular wall. Whatever the wall material may be the scanning surface appears to be an acoustic reflector as a consequence of low air impedance. The realized device is able to give distance information in each motor position and thus permits the derivation of a set of points as a ray trace-scanner. The dataset contains points lying on a circular arc and relating to strong returns. Three different approaches are herein considered to estimate both the slope of the wall and its minimum distance from the sensor. Slope and perpendicular distance are the parameters of a target plane, which may be calculated in each observer's position to predict its new location. Experimental tests and simulations are shown and discussed by scanning from different stationary locations. They allow the appreciation of the effectiveness of the proposed approaches
Clustering and PCA for Reconstructing Two Perpendicular Planes Using Ultrasonic Sensors
No full textIn this paper, the authors make use of sonar transducers to detect the corner of two orthogonal panels and they propose a strategy for accurately reconstructing the surfaces. In order to point a linear array of four sensors at the desired position, the motion of a digital motor is appropriately controlled. When the sensors are directed towards the intersection between the planes, longer times of flight are observed because of multiple reflections. All the concerned distances have to be excluded and that is why an indicator based on the output signal energy is introduced. A clustering technique allows for the partitioning of the dataset in three clusters and the indicator selects the subset containing misrepresented information. The remaining distances are corrected so as to take into consideration the directivity and they permit the plotting of two sets of points in a three-dimensional space. In order to leave out the outliers, each set is filtered by means of a confidence ellipsoid which is defined by the Principal Component Analysis (PCA). The best-fit planes are obtained based on the principal directions and the variances. Experimental tests and results are shown demonstrating the effectiveness of this new approach
Unevenness Point Descriptor for Terrain Analysis in Mobile Robot Applications
No full textIn recent years, the use of imaging sensors that produce a three-dimensional representation of the environment has become an efficient solution to increase the degree of perception of autonomous mobile robots. Accurate and dense 3D point clouds can be generated from traditional stereo systems and laser scanners or from the new generation of RGB-D cameras, representing a versatile, reliable and cost-effective solution that is rapidly gaining interest within the robotics community. For autonomous mobile robots, it is critical to assess the traversability of the surrounding environment, especially when driving across natural terrain. In this paper, a novel approach to detect traversable and non-traversable regions of the environment from a depth image is presented that could enhance mobility and safety through integration with localization, control and planning methods. The proposed algorithm is based on the analysis of the normal vector of a surface obtained through Principal Component Analysis and it leads to the definition of a novel, so defined, Unevenness Point Descriptor. Experimental results, obtained with vehicles operating in indoor and outdoor environments, are presented to validate this approach
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