Urban navigation of a mobile platform

This master thesis presents a method for 3D navigation of a robotic platform in urban environments. In autonomous navigation, the robot must know the localization of all the environment obstacles, so an algorithm for obstacle detection is developed and tested using a LiDAR and a camera as sensors, comparing the data points’ height. This detection focus on objects the robot could collide with in urban environments, including negative obstacles such as holes or stairs. The navigation and detection algorithms are all integrated in ROS (Robot Operating System). The simulation and experimental results show the effectiveness of the algorithm to detect those obstacles, being successful with the LiDAR as a sensor in urban environments, but not sufficient robust enough for the camera when the navigation is done outdoors with high sunlight

A High Throughput Integrated Hyperspectral Imaging and 3D Measurement System

Hyperspectral and three-dimensional measurements can obtain the intrinsic physicochemical properties and external geometrical characteristics of objects, respectively. The combination of these two kinds of data can provide new insights into objects, which has gained attention in the fields of agricultural management, plant phenotyping, cultural heritage conservation, and food production. Currently, a variety of sensors are integrated into a system to collect spectral and morphological information in agriculture. However, previous experiments were usually performed with several commercial devices on a single platform. Inadequate registration and synchronization among instruments often resulted in mismatch between spectral and 3D information of the same target. In addition, using slit-based spectrometers and point-based 3D sensors extends the working hours in farms due to the narrow field of view (FOV). Therefore, we propose a high throughput prototype that combines stereo vision and grating dispersion to simultaneously acquire hyperspectral and 3D information. Furthermore, fiber-reformatting imaging spectrometry (FRIS) is adopted to acquire the hyperspectral images. Test experiments are conducted for the verification of the system accuracy, and vegetation measurements are carried out to demonstrate its feasibility. The proposed system is an improvement in multiple data acquisition and has the potential to improve plant phenotyping