Abstract
Aiming at the high computational complexity of the traditional Rao-Blackwellized Particle Filtering (RBPF) method for simultaneous localization and Mapping (SLAM), an optimization method of RBPF-SLAM system is proposed, which is based on lidar and least square line segment feature extraction as well as raster, reliability mapping continuity. Validation test results show that less storage in constructing a map with this method is occupied, and the computational complexity is significantly reduced. The effect of noise data on feature data extraction results is effectively avoided. It also solves the problem of error accumulation caused by noninteger grid size movement of unmanned vehicle in time update stage based on Markov positioning scheme. The improved RBPF-SLAM method can enable the unmanned vehicle to construct raster map in real time, and the efficiency and accuracy of map construction are significantly improved.
Highlights
Nowadays, artificial intelligence technologies, such as unmanned driving, unmanned vehicle (UAV), virtual reality, and augmented reality, have entered people’s life, which cannot be without the development of localization and map building technology (SLAM)
SLAM based on lidar has become a widely used technology in unmanned vehicle positioning solutions due to its advantages of accurate measurement, no need to preset the scene, integration of multiple sensors, working in poor light environment, and generation of environment map for easy navigation. e technical basis of Lidar SLAM is the key to solving SLAM process, which mainly focuses on environmental feature extraction, data association, map creation, autonomous positioning, path planning, and other major problems
IEPF algorithm based on Hough transform is a common and efficient recursive algorithm to extract the point set of laser SLAM into line segment features
Summary
Artificial intelligence technologies, such as unmanned driving, UAV, virtual reality, and augmented reality, have entered people’s life, which cannot be without the development of localization and map building technology (SLAM). IEPF algorithm based on Hough transform is a common and efficient recursive algorithm to extract the point set of laser SLAM into line segment features. A line segment feature optimization extraction method based on Hough transform is proposed, which solves the problem that the extracted line segment feature parameters are not precise enough and provides a basis for SLAM map creation. Is method combines the advantages of PF and EKF, reduces the computational complexity, and has a good effect on data association On this basis, Monte Merlo et al [8] proposed the FAST-Slam1.0 algorithm based on RBPF. An observation update model based on similarity comparison is proposed, as well as a Markov localization method which can be located in a created line segment feature map. Analysis through simulation experiment shows that the method can effectively locate unmanned vehicles in feature map
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