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Robotic Navigation & Mapping w/Radar

Robotic Navigation & Mapping w/Radar

By (author)s: Martin Adams, Ebi Jose, Ba-Ngu Vo
Copyright: 2012
Pages: 330
ISBN: 9781608074822

Print Book £139.00 Qty:
eBook £92.00
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Focusing on autonomous robotic applications, this cutting-edge resource offers you a practical treatment of short-range radar processing for reliable object detection at the ground level. This unique book demonstrates probabilistic radar models and detection algorithms specifically for robotic land vehicles. It examines grid based robotic mapping with radar based on measurement likelihood estimation. You find detailed coverage of simultaneous localization and Map Building (SLAM) - an area referred to as the Holy Grailù of autonomous robotic research. The book derives an extended Kalman Filter SLAM algorithm which exploits the penetrating ability of radar. This algorithm allows for the observation of visually occluded objects, as well as the usual directly observed objects, which contributes to a robot's position and the map state update. Moreover, you discover how the Random Finite Set (RFS) provides a more appropriate approach for representing radar based maps than conventional frameworks.
Introduction - Isn 't Navigation and Mapping with Radar Solved? Why Radar in Robotics? Motivation. The Direction of Radar-Based Robotics Research. Structure of the Book. ; A Brief Overview of Radar Fundamentals -Introduction. Radar Measurements. The Radar Equation. Radar Signal Attenuation. Measurement Power Compression and Range Compensation. Radar-Range Measurement Techniques. Sources of Uncertainty in Radar. Uncertainty Specific to TOF and FMCW Radar. Polar to Cartesian Data Transformation. Summary. Bibliographical Remarks. ; AnIntroduction to Detection Theory -Introduction. Concepts of Detection Theory. Different Approaches to Target Detection. Detection Theory with Known Noise Statistics. Detection with Unknown Noise StatisticsAdaptive CFAR Processors. Summary. Bibliographical Remarks. ; Robotic Navigation and Mapping -Introduction. General Bayesian SLAMThe Joint Problem. Solving Robot Mapping and Localization Individually. Popular Robotic Mapping Solutions. Relating Sensor Measurements to Robotic Mapping and SLAM. Popular FB-SLAM Solutions. FBRM and SLAM with Random Finite Sets. SLAM and FBRM Performance Metrics. Summary. Bibliographical Remarks. ; Predicting and Simulating FMCW Radar Measurements -Introduction. FMCW Radar Detection in the Presence of Noise. Noise Distributions During Target Absence and Presence. Predicting Radar Measurements. Quantitative Comparison of Predicted and Actual Measurements. A-scope Prediction Results. Summary. Bibliographical Remarks. ; Reducing Detection Errors and Noise with Multiple Radar Scans -Introduction. Radar Data in an Urban Environment. Classical Scan Integration Methods. Integration Based on Target Presence Probability (TPP) Estimation. False Alarm and Detection Probabilities for the TPP Estimator. A Comparison of Scan Integration Methods. A Note on Multi-Path Reflections. TPP Integration of Radar in an Urban Environment. Recursive A-Scope Noise Reduction. Summary. Bibliographical Remarks. ; Grid-Based Robotic Mapping with Detection Likelihood Filtering -Introduction. The Grid-Based Robotic Mapping (GBRM) Problem. Mapping with Unknown Measurement Likelihoods. GBRM-ML Particle Filter Implementation. Comparisons of Detection and Spatial-Based GBRM. Summary. Bibliographical Remarks.; Feature-Based Robotic Mapping with Random Finite Sets -Introduction. The Probability Hypothesis Density (PHD)-FBRM Filter. PHD-FBRM Filter Implementation. PHD-FBRM Computational Complexity. Analysis of the PHD-FBRM Filter. Summary. Bibliographical Remarks. ; Radar-Based SLAM with Random Finite Sets -Introduction. SLAM with the PHD Filter. Implementing the RB-PHD-SLAM Filter. RB-PHD-SLAM Computational Complexity. Radar-Based Comparisons of RFS and Vector-Based SLAM. Summary. Bibliographical Remarks. ; X-Band Radar-Based SLAM in an Off-Shore Environment -Introduction. The ASC and the Coastal Environment. Marine Radar Feature Extraction. The Marine Based SLAM Algorithms. Comparisons of SLAM Concepts at Sea. Summary. Bibliographical Remarks. ;
  • Martin Adams Martin Adams is professor in the Department of Electrical Engineering, and a member of the Advanced Mining Technology Centre (AMTC) at the University of Chile. He received his D.Phil. in engineering science at the University of Oxford.
  • Ebi Jose Ebi Jose is a research scientist at Projective Space Pte. Ltd., Singapore. He holds a Ph.D. in electrical & electronic engineering from Nanyang Technological University.
  • Ba-Ngu Vo Ba-Ngu Vo is professor at the School of Electrical, Electronic & Computer Engineering at the University of Western Australia. He received his Ph.D. in engineering from Curtin University.
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