在线行人特征标签方法改进同步定位和制图技术——用于行人导航和建筑物自动测绘.pptVIP

Improving Simultaneous Localization and Mapping for Pedestrian Navigation and Automatic Mapping of Buildings by using Online Human-Based Feature Labeling Patrick Robertson, Michael Angermann, Mohammed Khider, German Aerospace Center (DLR) Slides from: “Improving Simultaneous Localization and Mapping for Pedestrian Navigation and Automatic Mapping of Buildings by using Online Human-Based Feature Labeling”, in Proc. IEEE/ION PLANS 2010, May 2010, Palm Springs, CA, USA. SLAM in Robotics Simultaneous Localization and Mapping - identified by robotics community in mid ‘80s! Premise: Localization using odometry and sensing of known landmarks is easy! Mapping of landmarks given known location and orientation (pose) is easy! Simultaneous Localization and Mapping is hard! What about SLAM for Humans? Human pedestrians are not robots but share some similarities with them Visual sensors (eyes) Odometry (in humans: sensed by proprioception), can be measured using inertial sensors Path and planning and execution For humans: little or no direct access to senses and functions Our central assumption: The pedestrian is able to actively control motion without violating physical constraints (i.e. walls, etc) Raw NavShoe Odometry Results FootSLAM Human-Recognizable Places An Example of Placestamps The PlaceSLAM Dynamic Bayesian Network (DBN) Intuitive Explanation of the Sequential Monte Carlo Estimator FootSLAM lets particles, or hypotheses, explore the state space of odometry errors, like evolution of drift as well as the association of places In this way, every particle is trying a slightly “differently bent piece of wire” Particles are weighted by their “compatibility” with their individual PlaceSLAM map their individual FootSLAM map optional sensor readings, such as GPS, magnetometer We can show that this is optimal in the Bayesian sense! Illustration of Proposal Function 1 Illustration of Proposal Function 2 Algorithm Summary Weight update Intuitive Illustration Intuitive Il