Sensor Modelling, Design and Data Processing for Autonomous Navigation
This invaluable book presents an unbiased framework for modelling and using sensors to aid mobile robot navigation. It addresses the problem of accurate and reliable sensing in confined environments and makes a detailed analysis of the design and construction of a low cost optical range finder. This is followed by a quantitative model for determining the sources and propagation of noise within the sensor. The physics behind the causes of erroneous data is also used to derive a model for detecting and labelling such data as false. In addition, the author's data-processing algorithms are applied to the problem of environmental feature extraction. This forms the basis of a solution to the problem of mobile robot localisation. The book develops a relationship between the kinematics of a mobile robot during the execution of successive manoeuvres, and the sensed features. Results which update a mobile vehicle's position using features from 2D and 3D scans are presented.
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Range Sensing in Confined Environments
Lidar Sensor Design Electronic Requirements
Lidar Sensor Design Mechanical and Optical
Quantitative Sensor Modelling Noise Analysis
Qualitative Sensor Modelling False Data
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3D scan A.M.C.W. lidar algorithm amplitude modulated analysis angle axis band-width calibration camera chapter circuit corresponding covariance matrix data point derived detected edges detector diode edge detection effect electronic end conditions environment equation extracted feature Fresnel lens function geometry Hence infra-red beam input intermediate frequency Lambert's cosine law laser lens lidar sensors light beam method metres mobile robot localisation mobile robot navigation modulated motor necessary observation optical beam path planning phase shift photo-diode photon noise PIN diodes pixel positional estimates possible predicted priori map problem produced range data range estimate range measurements range reading range variance RCD's received light received signal recorded reference signal relationship relative phase result returned signal strength samples scan in figure scanning mirror sensing sensor data shot noise shown in figure shows signal amplitude signal strength solution sonar square wave surface reflectivity target technique tracking transmitted light triangulation voltage
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Active Sensors for Local Planning in Mobile Robotics
Penelope Probert Smith
No preview available - 2001