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Full-Text Articles in Engineering

Object Classication, Detection And State Estimation Using Yolo V3 Deep Neural Network And Sensor Fusion Of Stereo Camera And Lidar, Kamalkumar Bharatkumar Mehta Aug 2021

Object Classication, Detection And State Estimation Using Yolo V3 Deep Neural Network And Sensor Fusion Of Stereo Camera And Lidar, Kamalkumar Bharatkumar Mehta

Mechanical and Aerospace Engineering Theses

Real-time object classification, localization, and detection with region-based convolution neural network (R-CNN) require high computational power, or it consumes a tremendous amount of time with the use of the available onboard computer system. Therefore, either of those ways is not practical in real-time object detection. In contrast, the YOLO-v3 network, which stands for you only look once, that uses the YOLO algorithm seems to be practical in live object classification, localization, and detection, since the YOLO algorithm seems to work faster than the sliding window algorithm used by R-CNN. Sensor fusion in this context requires estimation and association of information …


Ego-Localization Navigation For Intelligent Vehicles Using 360° Lidar Sensor For Point Cloud Mapping, Tyler Naes Jan 2017

Ego-Localization Navigation For Intelligent Vehicles Using 360° Lidar Sensor For Point Cloud Mapping, Tyler Naes

Electronic Theses and Dissertations

With its prospects of reducing vehicular accidents and traffic in highly populated urban areas by taking the human error out of driving, the future in automobiles is leaning towards autonomous navigation using intelligent vehicles. Autonomous navigation via Light Detection And Ranging (LIDAR) provides very accurate localization within a predefined, a priori, point cloud environment that is not possible with Global Positioning System (GPS) and video camera technology. Vehicles may be able to follow paths in the point cloud environment if the baseline paths it must follow are known in that environment by referencing objects detected in the point cloud …


Algorithms For Lidar Based Traffic Tracking: Development And Demonstration, Vamsi K. Bandaru Aug 2016

Algorithms For Lidar Based Traffic Tracking: Development And Demonstration, Vamsi K. Bandaru

Open Access Theses

The current state of the art of traffic tracking is based on the use of video, and requires extensive manual intervention for it to work, including hours of painstaking human examination of videos frame by frame which also make the acquisition of data extremely expensive. Fundamentally, this is because we do not have observability of the actual scene from a camera which captures a 2D projection of the 3D world. Even if video were to be automated, it would involve such algorithms as RANSACK for outlier elimination while matching features across frames or across multiple cameras. This results in algorithms …


Verification Of A Dual-State Extended Kalman Filter With Lidar-Enabled Autonomous Hazard-Detection For Planetary Landers, Peter Joseph Jorgensen Apr 2015

Verification Of A Dual-State Extended Kalman Filter With Lidar-Enabled Autonomous Hazard-Detection For Planetary Landers, Peter Joseph Jorgensen

Master's Theses (2009 -)

This thesis presents a mathematical model for a LIDAR-enabled Terrain- and Hazard-Relative Navigation sensor and the design and implementation of a dual-state extended Kalman filter. The extended Kalman filter equations are presented in summary. Mathematical models for an altimeter, a velocimeter, a star tracker, and a lidar-enabled mapping/tracking sensor are presented in depth. An explanation of the software designed for computer simulation is included. It is proved through this analysis that, when implemented as part of a well-tuned extended Kalman Filter and in combination with other sensors, the proposed model for a lidar-enabled mapping/tracking sensor significantly reduces estimation error. This …