## 2 Problem Statement  
The challenge in autonomous navigation of a UAV in urban environment is recognizing and localizing obstacles at the right time and continuously adjusting the path of the UAV in such a way that it can avoid the obstacles and navigate to the destination safely. To this end, it requires integrating effective object detection and path planning algorithms that run on a companion computer onboard the UAV.  
Most of the widely used object detection algorithms are based on scanning the entire environment and discretizing the scanned region to create a dense mesh of grid points from which objects are detected. This process requires a companion computer with high storage capacity and intensive computational power. Moreover, the well-known path-planning algorithms either randomly sample or exhaustively explore the entire consecutive obstacle-free grid points to generate optimal path towards destination. This incurs additional computational burden on companion computer and compromises the real-timeness of the navigation commands. [PERSON] et al. [14] conducted a comprehensive review on the most popular 3D path planing algorithms. In their review, a detailed analysis of the advantages and disadvantages of these commonly used algorithms is given. They reported that despite the intensive applications of these algorithms, the problem of real-time path planning in a cluttered environment remains unsolved.  
[PERSON] et al. [15] proposed light-weight CNN-based network structure for both object detection and safe autonomous navigation of a UAV in indoor/outdoor environments. However, the whole process of object detection and UAV path planning was performed on a ground-based computer and communication with the UAV was through a Wifi connection. This had a catastrophic drawback on the safe navigation of the UAV in indoor environment where Wifi connection failure is likely. Moreover, the Wifi data transfer rate may create a delay in navigation commands to be sent to the UAV. In an attempt to remove the dependency of the UAV on ground-based commands, [PERSON] et al. [16] proposed a UAV framework for autonomous navigation in a cluttered indoor environment based on companion computer on-board the UAV. The performance of this framework was validated through hardware-in-the-loop simulation, and it appears to be promising to put an end to ground-based navigation command. However, an occupancy map of the cluttered environment in which the UAV navigated was pre-loaded on the companion computer. This undermines the applicability of the framework in dynamic environment.  
To avoid computational burden on the companion computer, [PERSON] et al. [17] applied a data-driven approach, where data about the cluttered environment must be collected prior to the UAV mission. As proposed in their work, DroNet makes use of the collected data and safely navigates a UAV in the streets of a city. However, this approach, again, has limitations when it comes to dynamic or unknown environments.  
This study, therefore, tends to address the challenges of computational burden subjected to companion computer onboard a UAV by integrating the available fastest object detection algorithm and the proposed light-weight real-time 3D path planner. Such an approach by-passes the challenges of dynamic or unknown environments. In the preliminary performance test, we assumed limited number of objects: pedestrian, window, electric poles, tunnel, trees, and barely visible nets as plausible obstacles that the UAV may encounter in a disaster monitoring scenario. Once the proposed 3D path planner is validated in a complete real-flight tests, further objects will be included in the machine learning process.