State Estimation for Robotics

Timothy D. Barfoot

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商品描述

A key aspect of robotics today is estimating the state, such as position and orientation, of a robot as it moves through the world. Most robots and autonomous vehicles depend on noisy data from sensors such as cameras or laser rangefinders to navigate in a three-dimensional world. This book presents common sensor models and practical advice on how to carry out state estimation for rotations and other state variables. It covers both classical state estimation methods such as the Kalman filter, as well as important modern topics such as batch estimation, the Bayes filter, sigmapoint and particle filters, robust estimation for outlier rejection, and continuous-time trajectory estimation and its connection to Gaussian-process regression. The methods are demonstrated in the context of important applications such as point-cloud alignment, pose-graph relaxation, bundle adjustment, and simultaneous localization and mapping. Students and practitioners of robotics alike will find this a valuable resource.

商品描述(中文翻譯)

如今,機器人學的一個關鍵方面是估計機器人在移動過程中的狀態,例如位置和方向。大多數機器人和自主車輛依賴於來自相機或激光測距儀等傳感器的噪聲數據來在三維世界中導航。本書介紹了常見的傳感器模型,並提供了關於如何進行旋轉和其他狀態變量的狀態估計的實用建議。它涵蓋了傳統的狀態估計方法,如卡爾曼濾波器,以及重要的現代主題,如批次估計、貝葉斯濾波器、sigma點和粒子濾波器、用於拒絕異常值的魯棒估計,以及連續時間軌跡估計及其與高斯過程回歸的聯繫。這些方法在重要應用領域中得到了演示,例如點雲對齊、姿態圖放鬆、束調整和同時定位與地圖構建。無論是機器人學的學生還是從業人員都會發現這是一個有價值的資源。