Udacity CS373：无人驾驶汽车编程学习笔记一

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一：定位

蒙特卡罗定位是感知和运动的循环，每次感知都会获得信息，每次运动都会丢失信息，感知函数利用了贝叶斯规则，运动函数利用了全概率定理。

基于一维的蒙特卡罗定位程序如下：

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p = [0, 1, 0, 0, 0)
world = ['green', 'red', 'red', 'green', 'green']
measurements = ['red', 'green']
motions = [1, 1]
pHit = 0.6
pMiss = 0.2
pExact = 0.8
pOvershoot = 0.1
pUndershoot = 0.1


def sense(p, Z):
    q = []
    for i in range(len(p)):
        hit = (Z == world[i])
        q.append(p[i] * (hit * pHit + (1-hit) * pMiss))
    s = sum(q)
    for i in range(len(p)):
        q[i] = q[i]/s
    return q


def move(p, U):
    q = []
    for i in range(len(p)):
        s = pExact * p[(i-U) % len(p)]
        s = s + pOvershoot * p[(i-U-1) % len(p)]
        s = s + pUndershoot * p[(i-U+1) % len(p)]
        q.append(s)
    return q


for k in range(len(measurements)):
    p = sense(p, measurements[k])
    p = move(p, motions[k])
    print p