Signals, such as point clouds captured by light detection and ranging sensors, are often affected by highly reflective objects, including specular opaque and transparent materials, such as glass, mirrors, and polished metal, which produce reflection artifacts, thereby degrading the performance of associated computer vision techniques. In traditional noise filtering methods for point clouds, noise is detected by considering the distribution of the neighboring points. However, noise generated by reflected areas is quite dense and cannot be removed by considering the point distribution. Therefore, this paper proposes a noise removal method to detect dense noise points caused by reflected objects using multi-position sensing data comparison. The proposed method is divided into three steps. First, the point cloud data are converted to range images of depth and reflective intensity. Second, the reflected area is detected using a sliding window on two converted range images. Finally, noise is filtered by comparing it with the neighbor sensor data between the detected reflected areas. Experiment results demonstrate that, unlike conventional methods, the proposed method can better filter dense and large-scale noise caused by reflective objects. In future work, we will attempt to add the RGB image to improve the accuracy of noise detection.
IntroductionInternet of things (IoT) is growing fast in over the world [1][2][3][4][5][6][7]. In an IoT-based system for the autonomous vehicles, light detection and ranging (Lidar) sensors are often used to collect data of surrounding environments. Furthermore, in human-centric autonomous systems, robots also have several attached cameras and an inertial measurement unitglobal positioning system (IMU-GPS) sensor. In each frame, the Lidar sensor returns a point cloud that describes the terrain around the robot. The data from the Lidar sensor are transferred to a computer and split into two groups: ground and nonground. The first group includes ground points of terrain which a robot can traverse. On the other hand, the second group consists of nonground points which the robot cannot traverse such as cars, trees, walls, etc. If the terrain is sloping such that the autonomous robot cannot traverse it, the corresponding points are clustered into the nonground group. The segmentation of three-dimensional (3D) point cloud ground data is a fundamental AbstractGround segmentation is an important step for any autonomous and remote-controlled systems. After separating ground and nonground parts, many works such as object tracking and 3D reconstruction can be performed. In this paper, we propose an efficient method for segmenting the ground data of point clouds acquired from multi-channel Lidar sensors. The goal of this study is to completely separate ground points and nonground points in real time. The proposed method segments ground data efficiently and accurately in various environments such as flat terrain, undulating/ rugged terrain, and mountainous terrain. First, the point cloud in each obtained frame is divided into small groups. We then focus on the vertical and horizontal directions separately, before processing both directions concurrently. Experiments were conducted, and the results showed the effectiveness of the proposed ground segment method. For flat and sloping terrains, the accuracy is over than 90%. Besides, the quality of the proposed method is also over than 80% for bumpy terrains. On the other hand, the speed is 145 frames per second. Therefore, in both simple and complex terrains, we gained good results and real-time performance.
Nowadays, deep learning methods based on a virtual environment are widely applied to research and technology development for autonomous vehicle’s smart sensors and devices. Learning various driving environments in advance is important to handle unexpected situations that can exist in the real world and to continue driving without accident. For training smart sensors and devices of an autonomous vehicle well, a virtual simulator should create scenarios of various possible real-world situations. To create reality-based scenarios, data on the real environment must be collected from a real driving vehicle or a scenario analysis process conducted by experts. However, these two approaches increase the period and the cost of scenario generation as more scenarios are created. This paper proposes a scenario generation method based on deep learning to create scenarios automatically for training autonomous vehicle smart sensors and devices. To generate various scenarios, the proposed method extracts multiple events from a video which is taken on a real road by using deep learning and generates the multiple event in a virtual simulator. First, Faster-region based convolution neural network (Faster-RCNN) extracts bounding boxes of each object in a driving video. Second, the high-level event bounding boxes are calculated. Third, long-term recurrent convolution networks (LRCN) classify each type of extracted event. Finally, all multiple event classification results are combined into one scenario. The generated scenarios can be used in an autonomous driving simulator to teach multiple events that occur during real-world driving. To verify the performance of the proposed scenario generation method, experiments using real driving video data and a virtual simulator were conducted. The results for deep learning model show an accuracy of 95.6%; furthermore, multiple high-level events were extracted, and various scenarios were generated in a virtual simulator for smart sensors and devices of an autonomous vehicle.
To develop a realistic simulator for autonomous vehicle testing, the simulation of various scenarios that may occur near vehicles in the real world is necessary. In this paper, we propose a new scenario generation pipeline focused on generating scenarios in a specific area near an autonomous vehicle. In this method, a scenario map is generated to define the scenario simulation area. A convolutional neural network (CNN)-based scenario agent selector is introduced to evaluate whether the selected agents can generate a realistic scenario, and a collision event detector handles the collision message to trigger an accident event. The proposed event-centric action dispatcher in the pipeline enables agents near events to perform related actions when the events occur near the autonomous vehicle. The proposed scenario generation pipeline can generate scenarios containing pedestrians, animals, and vehicles, and, advantageously, no user intervention is required during the simulation. In addition, a virtual environment for autonomous driving is also implemented to test the proposed scenario generation pipeline. The results show that the CNN-based scenario agent selector chose agents that provided realistic scenarios with 92.67% accuracy, and the event-centric action dispatcher generated a visually realistic scenario by letting the agents surrounding the event generate related actions.
With intelligent big data, a variety of gesture-based recognition systems have been developed to enable intuitive interaction by utilizing machine learning algorithms. Realizing a high gesture recognition accuracy is crucial, and current systems learn extensive gestures in advance to augment their recognition accuracies. However, the process of accurately recognizing gestures relies on identifying and editing numerous gestures collected from the actual end users of the system. This final end-user learning component remains troublesome for most existing gesture recognition systems. This paper proposes a method that facilitates end-user gesture learning and recognition by improving the editing process applied on intelligent big data, which is collected through end-user gestures. The proposed method realizes the recognition of more complex and precise gestures by merging gestures collected from multiple sensors and processing them as a single gesture. To evaluate the proposed method, it was used in a shadow puppet performance that could interact with on-screen animations. An average gesture recognition rate of 90% was achieved in the experimental evaluation, demonstrating the efficacy and intuitiveness of the proposed method for editing visualized learning gestures.
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