上次分析的地方有误，应该注意，不好意思，来重新分析一下这一段，
分析之前，把上次的知识回锅一下：里程计发布这一时刻的数据，然后粒子根据里程计数据更析自己状态，但是每
一个粒子对应的probability都设置为0
好，那就开始分析吧，
threshold = WORST_POSSIBLE-1; // ensures that we accept anything in 1st round
for (p = 0; p < PASSES; p++){
best = 0;
for (i = 0; i < SAMPLE_NUMBER; i++) {
if (newSample[i].probability >= threshold) {
for (k = p; k < SENSE_NUMBER; k += PASSES)
newSample[i].probability = newSample[i].probability + log(QuickScore(sense, k, i));
if (newSample[i].probability > newSample[best].probability)
best = i;
}
else
newSample[i].probability = WORST_POSSIBLE;
}
threshold = newSample[best].probability - THRESH;
}
从这段代码中可以了解：
1，PASSES是筛选粒子的轮数，一共筛选粒子(PASSES - 1)轮
2，在第一轮中(PASSES = 0)，threshold设为负值，保留所有粒子
3，SAMPLE_NUMBER指粒子个数，SENSE_NUMBER指雷达传感器数据
4，凡是合格的粒子，即粒子的概率大于threshold，都会进行下一轮评估，即把这一时刻的雷达数据拿来，做
“适应性”评价，用评价的分数计算该合格粒子的probability
评价函数就是之前介绍的QuickScore()
5，当所有合格粒子的probability都被更新一遍之后，自然要更新threshold
threshold的更新原则：用合格粒子最优的probability减去一个常数THRESH
用激光雷达数据更新过粒子的probability之后，有如下的操作，
keepers = 0;
for (i = 0; i < SAMPLE_NUMBER; i++) {
if (newSample[i].probability >= threshold) {
keepers++;
// Don't let this heuristic evaluation be included in the final eval.
newSample[i].probability = 0.0;
}
else
newSample[i].probability = WORST_POSSIBLE;
}
// Letting the user know how many samples survived this first cut.
fprintf(stderr, "Better %d ", keepers);
threshold = -1;
从这段代码中，可以了解到：1，keepers用来计数合格粒子，并且把合格粒子的probability置为0;
2, 把not pass的粒子的probability置为WORST_POSSIBLE，而它等于-10000 3，然后threshold置为 -1
接下来有，
// Now reevaluate all of the surviving samples, using the full laser scan to look for possible
// obstructions, in order to get the most accurate weights. While doing this evaluation, we can
// still keep our eye out for unlikely samples before we are finished.
keepers = 0;
for (p = 0; p < PASSES; p++){
best = 0;
for (i = 0; i < SAMPLE_NUMBER; i++) {
if (newSample[i].probability >= threshold) {
if (p == PASSES -1)
keepers++;
for (k = p; k < SENSE_NUMBER; k += PASSES)
newSample[i].probability = newSample[i].probability + log(CheckScore(sense, k, i));
if (newSample[i].probability > newSample[best].probability)
best = i;
}
else
newSample[i].probability = WORST_POSSIBLE;
}
threshold = newSample[best].probability - THRESH;
}
从这段代码可以了解到：
1，PASSES指粒子筛选的轮数（跟之前的筛选有些相似）
2，筛选的机制跟之前讨论的类似，同样，threshold更新机制还是一样
然后有，
// Report how many samples survived the second cut. These numbers help the user have confidence that
// the threshhold values used for culling are reasonable.
fprintf(stderr, "Best of %d ", keepers);
第二次筛选 称为 the second cut
筛选之后呢，
// All probabilities are currently in log form. Exponentiate them, but weight them by the prob of the
// the most likely sample, to ensure that we don't run into issues of machine precision at really small
// numbers.
total = 0.0;
threshold = newSample[best].probability;
for (i = 0; i < SAMPLE_NUMBER; i++) {
// If the sample was culled, it has a weight of 0
if (newSample[i].probability == WORST_POSSIBLE)
newSample[i].probability = 0.0;
else {
newSample[i].probability = exp(newSample[i].probability-threshold);
total = total + newSample[i].probability;
}
}
从这段代码可以了解到，
1，不合格的粒子的probability记为0
2，合格粒子的probability用指数表示
3，total的计算，是为了归一化，这也是重采样的过程之一
总而言之，这一段粒子筛选的代码就是这样的，
下次再说！
