We present an algorithm which can realize mobile robot in unknown outdoor environments, which 3D stereo vision simultaneous localization and mapping （SLAM） for means the 6-DOF motion and a sparse but persistent map of natural landmarks be constructed online only with a stereo camera. In mobile robotics research, we extend FastSLAM 2.0 like stereo vision SLAM with ＂pure vision＂ domain to outdoor environments. Unlike popular stochastic motion model used in conventional monocular vision SLAM, we utilize the ideas of structure from motion （SFM） for initial motion estimation, which is more suitable for the robot moving in large-scale outdoor, and textured environments. SIFT features are used as natural landmarks, and its 3D positions are constructed directly through triangulation. Considering the computational complexity and memory consumption, Bkd-tree and Best-Bin-First （BBF） search strategy are utilized for SIFT feature descriptor matching. Results show high accuracy of our algorithm, even in the circumstance of large translation and large rotation movements.
提出一种基于粒子滤波器的机器人定位算法.首先利用一并行扩展卡尔曼滤波器作为粒子预测分布,将当前观测的部分信息融入,以改善滤波效果,减小所需粒子数;然后提出变密度函数边界的马尔可夫链蒙特卡洛(Markov chain Monte Carlo,MCMC)重采样方法,以提高粒子的细化能力;最后结合普通重采样方法,提出一种改进的MCMC重采样的机器人定位算法,减少粒子匮乏效应的同时,提高了定位精度.实验结果表明,该算法较传统方法在计算复杂度、定位精度和鲁棒性方面都有显著提高.
