Path Optimization Method Using Fusion Depth Information and Nonlinear Pose Estimation

In the agricultural field spray application process, the traditional human spray, because of large amount of labor, toxic to human body, was gradually replaced by other spray methods. One of the most popular methods is the smart spray of mobile cars. For autonomous driving vehicles applied with intelligent variable spray, the detection and accurate positioning of feature points play an important role in autonomous driving of robots. Feature detection is equivalent to the eyes of the car to obtain plant information, road condition. Accurate positioning is equivalent to the brain of the car. After the car acquires color information and depth information, it finds its exact position and guides the car to drive independently. In the process of continuous development of the visual synchronous localization algorithm of self-propelled vehicle, the traditional path optimization based on the traditional filtering form has the phenomenon of poor positioning accuracy and floating point drift. For the stable running of the car, precise spray has a great impact. To solve this problem, a method of global nonlinear optimization with depth information was proposed. The RealSense camera was used to obtain continuous color and depth information frames in real time. Firstly, through the continuous color information frames obtained, the FAST feature points of the overlapped part were extracted, the scale invariance and rotation invariance were optimized, and the BRIEF description was calculated to obtain the feature description of two consecutive key frame repetition regions. Then, feature matching was performed by the nearest neighbor algorithm, and Nanoflann algorithm was used to accelerate the matching process. After obtaining the matching point pair of continuous key frames, the minimum distance method was used to screen the mismatched points, and the random sampling consistency method (RANSAC) based on the basic matrix was used to test the matching point pair. After eliminating the false match and obtaining the correct match point, PnP was used to calculate the pose change of continuous key frames, calculate the residual error, and build the incremental equation. Dogleg algorithm was used to estimate the pose of continuous key frames for multiple iterations and optimization to obtain the precise pose of spray car. At the same time, in the process of calculating the residual error iterative optimization, the bit-pose calculated by the RealSense acquisition depth information and the bit-pose calculated by the polar constraint solution were integrated into the iterative optimization. Compared with the single depth information correction mode, the algorithm effectively improved the positioning accuracy of the car. When the depth information collection was lost, the polar constraint compensated the process of vehicle posture estimation, and improved the robustness of accurate real-time acquisition of vehicle track. © 2019, Chinese Society of Agricultural Machinery. All right reserved.