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使用placement new来提高效率
将代码中多次构造mat对象的地方,首先在栈上创建一个足够大的区域,每次将临时变量构造在该地址
YawSpeed and PitchSpeed
we need to use the information provided by lower machine, actually the yaw spin speed(round/per seconds) and pitch spin speed, to adjust the Kalman filter
4-4测试
- Intel D435相机驱动写在子线程里一堆错误,且会因为存活域爆出不同错误,需要定制一版代码。
毁灭吧,世界!
3-30测试
- 数据接受和发送会有BUG,但疑似已解决,是电控问题,导致数据接受停止在read函数。
- 在调试过程中改完代码后一定要整理,不然就会造成极难发现的遗留BUG,切记切记!
3.自启动和异常处理得加快了
4.小陀螺应对方案初步定为:识别小车,若小车区域内未发现装甲板,对车打,否则对装甲板打
4-2测试
- 哨兵反向运动时需清空预测参数之类
- 怀疑预测效果,需实际上机再次调试
关于误识别导致枪管抖动严重
通过识别角度振动变化或者电控回传旋转速度判断是否出现误识别情况,并选择策略消除抖动,如记录参数下次匹配时减小误识别概率
回传数据
在目标角度在视觉计算的情况下,由于电控相应时间的问题,delta值需乘一个系数
FindContours
There are not any OpenCV cuda function that is similar to findContours(), but someone suggest us to feed the function with the data from GPU Image's canny or Sobel edge. We can try and debug this!
3-27测试
存在误识别时抖动情况。装甲板长宽比限制需调整;神经网络可以加上
3-25 测试
根据距离补偿装甲板的长宽比;预测可以更激进;在未识别到时根据平方下降处理yaw和pitch偏移值
3-28测试
为应对将两个装甲板相邻灯条间的代码误识别为灯条的情况,可以将图像(曝光度低)在另一线程中调整至FLOAT类型,并增加对比度,再进行追踪,提供更好的视觉追踪条件,在识别后的装甲板中,可使用数字识别,若未识别到,可以将当前追踪器释放回到搜索模式并选择更两侧的灯条进行组装装甲板。
3-30
修改了一些识别逻辑,删除compare模块,计划将神经网络部分额外设计一个类,进行GPU计算,基本放弃在传统算法上使用GPU加速,但OPENCV可能提供了一些其他的GPU加速工具可以使用,如rapid模块等,修改了一些由于debug遗留造成的bug。
还有一件事,目前针对各相机的标定数据还需再标定一次。
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