Browsing by Subject "BLBX2"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Autonomous Embedded System Enabled 3-D Object Detector: (with Point Cloud and Camera)
    (IEEE, 2019-09) Katare, Dewant; El-Sharkawy, Mohamed; Electrical and Computer Engineering, School of Engineering and Technology
    An Autonomous vehicle or present day smart vehicle is equipped with several ADAS safety features such as Blind Spot Detection, Forward Collision Warning, Lane Departure and Parking Assistance, Surround View System, Vehicular communication System. Recent research utilize deep learning algorithms as a counterfeit for these traditional methods, using optimal sensors. This paper discusses the perception tasks related to autonomous vehicle, specifically the computer-vision approach of 3D object detection and thus proposes a model compatible with embedded system using the RTMaps framework. The proposed model is based on the sensors: camera and Lidar connected to an autonomous embedded system, providing the sensed inputs to the deep learning classifier which on the basis of theses inputs estimates the position and predicts a 3-d bounding box on the physical objects. The Frustum PointNet a contemporary architecture for 3-D object detection is used as base model and is implemented with extended functionality. The architecture is trained and tested on the KITTI dataset and is discussed with the competitive validation precision and accuracy. The Presented model is deployed on the Bluebox 2.0 platform with the RTMaps Embedded framework.
  • Thumbnail Image
    Item
    Real-Time 3-D Segmentation on An Autonomous Embedded System: using Point Cloud and Camera
    (IEEE, 2019-07) Katare, Dewant; El-Sharkawy, Mohamed; Electrical and Computer Engineering, School of Engineering and Technology
    Present day autonomous vehicle relies on several sensor technologies for it's autonomous functionality. The sensors based on their type and mounted-location on the vehicle, can be categorized as: line of sight and non-line of sight sensors and are responsible for the different level of autonomy. These line of sight sensors are used for the execution of actions related to localization, object detection and the complete environment understanding. The surrounding or environment understanding for an autonomous vehicle can be achieved by segmentation. Several traditional and deep learning related techniques providing semantic segmentation for an input from camera is already available, however with the advancement in the computing processor, the progression is on developing the deep learning application replacing traditional methods. This paper presents an approach to combine the input of camera and lidar for semantic segmentation purpose. The proposed model for outdoor scene segmentation is based on the frustum pointnet, and ResNet which utilizes the 3d point cloud and camera input for the 3d bounding box prediction across the moving and non-moving object and thus finally recognizing and understanding the scenario at the point-cloud or pixel level. For real time application the model is deployed on the RTMaps framework with Bluebox (an embedded platform for autonomous vehicle). The proposed architecture is trained with the CITYScpaes and the KITTI dataset.