Visual SLAM in dynamic environments based on object detection

A great number of visual simultaneous localization and mapping (VSLAM) systems need to assume static features in the environment. However, moving objects can vastly impair the performance of a VSLAM system which relies on the static-world assumption. To cope with this challenging topic, a real-time and robust VSLAM system based on ORB-SLAM2 for dynamic environments was proposed. To reduce the influence of dynamic content, we incorporate the deep-learning-based object detection method in the visual odometry, then the dynamic object probability model is added to raise the efficiency of object detection deep neural network and enhance the real-time performance of our system. Experiment with both on the TUM and KITTI benchmark dataset, as well as in a real-world environment, the results clarify that our method can significantly reduce the tracking error or drift, enhance the robustness, accuracy and stability of the VSLAM system in dynamic scenes.     

基于SLAM的图形建模仿真环境

本文详细介绍了基于ＳＬＡＭ的图形建模仿真环境的设计与应用，包括图形建模环境的实现，ＳＬＡＭ网络的进程交互仿真策略以及仿真器的设计，并进一步探讨了该系统用于协同仿真进行混合异构建模的思想。     

Research on simultaneous localization and mapping for AUV by an improved method: Variance reduction FastSLAM with simulated annealing

At present, simultaneous localization and mapping (SLAM) for an autonomous underwater vehicle (AUV) is a research hotspot. Aiming at the problem of non-linear model and non-Gaussian noise in AUV motion, an improved method of variance reduction fast simultaneous localization and mapping (FastSLAM) with simulated annealing is proposed to solve the problems of particle degradation, particle depletion and particle loss in traditional FastSLAM, which lead to the reduction of AUV location estimation accuracy. The adaptive exponential fading factor is generated by the anneal function of simulated annealing algorithm to improve the effective particle number and replace resampling. By increasing the weight of small particles and decreasing the weight of large particles, the variance of particle weight can be reduced, the number of effective particles can be increased, and the accuracy of AUV location and feature location estimation can be improved to some extent by retaining more information carried by particles. The experimental results based on trial data show that the proposed simulated annealing variance reduction FastSLAM method avoids particle degradation, maintains the diversity of particles, weakened the degeneracy and improves the accuracy and stability of AUV navigation and localization system.