In recent years, there have been a lot of interests in incorporating semantics into simultaneous localization and mapping （SLAM） systems. This paper presents an approach to generate an outdoor large-scale 3D dense semantic map based on binocular stereo vision. The inputs to system are stereo color images from a moving vehicle. First, dense 3D space around the vehicle is constructed, and tile motion of camera is estimated by visual odometry. Meanwhile, semantic segmentation is performed through the deep learning technology online, and the semantic labels are also used to verify tim feature matching in visual odometry. These three processes calculate the motion, depth and semantic label of every pixel in the input views. Then, a voxel conditional random field （CRF） inference is introduced to fuse semantic labels to voxel. After that, we present a method to remove the moving objects by incorporating the semantic labels, which improves the motion segmentation accuracy. The last is to generate tile dense 3D semantic map of an urban environment from arbitrary long image sequence. We evaluate our approach on KITTI vision benchmark, and the results show that the proposed method is effective.
Yi YangFan QiuHao LiLu ZhangMei-Ling WangMeng-Yin Fu
Parking is an important and indispensable skill for drivers. With rapid urban development, the automatic parking assistant system is one of the key components in future automobiles. Path planning is always essential for solving parking problems. In this paper, a path planning method is proposed for parking using straight lines and circular curves of different radius without collisions with obstacles. The parking process is divided into two steps in which the vehicle reaches the goal state through the intermediate state from the initial state. The intermediate state will be selected from the intermediate state set with a certain criterion in order to avoid obstacles. Similarly, an appropriate goal state will be selected based on the size of the parking lot. In addition, an automatic parking system is built, which effectively achieves the parking lot perception, path planning and performs parking processes in the environment with obstacles. The result of simulations and experiments demonstrates the feasibility and practicality of the proposed method and the automatic parking system.
Designing a stable and robust flight control system for an Unmanned Aerial Vehicle(UAV)is an arduous task.This paper addresses the trajectory tracking control problem of a Ducted Fan UAV(DFUAV)using offset-free Model Predictive Control(MPC)technique in the presence of various uncertainties and external disturbances.The designed strategy aims to ensure adequate flight robustness and stability while overcoming the effects of time delays,parametric uncertainties,and disturbances.The six degrees of freedom DFUAV model is divided into three flight modes based on its airspeed,namely the hover,transition,and cruise mode.The Dryden wind turbulence is applied to the DFUAV in the linear and angular velocity component.Moreover,different uncertainties such as parametric,time delays in state and input,are introduced in translational and rotational components.From the previous work,the Linear Quadratic Tracker with Integrator(LQTI)is used for comparison to corroborate the performance of the designed controller.Simulations are computed to investigate the control performance for the aforementioned modes and different flight phases including the autonomous flight to validate the performance of the designed strategy.Finally,discussions are provided to demonstrate the effectiveness of the given methodology.
