激光雷达数学模型和运动畸变去除
文章目录
- 1 概念介绍
- 1.1 激光雷达传感器
- 1.2 激光雷达数学模型
- 1.3 运动畸变
- 2 畸变去除
- 2.1 纯估计方法
- 2.2 里程计辅助方法
- 2.3 融合方法
- 3 实现一个激光雷达运动畸变去除模块
- 3.1 自定义激光雷达消息
- 3.2 编译激光雷达运动畸变去除包
- 3.3 播放bag
- 3.4 效果
- 3.5 代码解析
- 3.5.1 头文件
- 3.5.2 主函数
- 3.5.3 定义LidarMotionCalibrator类
- 3.5.4 构造函数LidarMotionCalibrator
- 3.5.5 回调函数ScanCallBack
- 3.5.6 获取机器人某时刻在里程计坐标系下的位姿
- 3.5.7 对激光雷达数据进行分段
- 3.5.8 对分段数据插值后进行去畸变
- 4 参考
1 概念介绍
1.1 激光雷达传感器
1.2 激光雷达数学模型
1.3 运动畸变
2 畸变去除
2.1 纯估计方法
2.2 里程计辅助方法
2.3 融合方法
3 实现一个激光雷达运动畸变去除模块
3.1 自定义激光雷达消息
自定义激光雷达消息ChampionNavLaserScan.msg：
Header header
float32 angle_min
float32 angle_max
float32 scan_time
float32 time_increment
float32 range_min
float32 range_max
float32[] ranges
float32[] angles
float32[] intensities
为了能够在其他package中使用此message，需要首先进行编安装。
cd ~/LaserMotion/champion_nav_msgs/src
catkin_init_workspace
cd ..
sudo su
source /opt/ros/kinetic/setup.bash
catkin_make -DCMAKE_INSTALL_PREFIX=/opt/ros/kinetic install
3.2 编译激光雷达运动畸变去除包
cd ~/LaserUndistortion_ws
catkin_make
source devel/setup.bash
roslaunch LaserUndistortion LaserUndistortion.launch
注意：必须保证没有任何ROS节点在运行，roscore也要关闭。
如果一切正常，则会看到PCL的可视化界面。
3.3 播放bag
rosbag play --clock odom.bag
3.4 效果
当可视化界面中存在数据时，按R键即可看到结果。红色为畸变矫正前，绿色为畸变矫正后。
3.5 代码解析
3.5.1 头文件
#include 3.5.2 主函数
int main(int argc, char **argv)
{
ros::init(argc, argv, "LidarMotionCalib");
tf::TransformListener tf(ros::Duration(10.0));
LidarMotionCalibrator tmpLidarMotionCalib(&tf);
ros::spin();
return 0;
}
3.5.3 定义LidarMotionCalibrator类
3.5.4 构造函数LidarMotionCalibrator
接收激光雷达数据champion_scan，在回调函数ScanCallBack中进行去除运动畸变的处理。
LidarMotionCalibrator(tf::TransformListener* tf)
{
tf_ = tf;
scan_sub_ = nh_.subscribe("champion_scan", 10, &LidarMotionCalibrator::ScanCallBack, this);
}
3.5.5 回调函数ScanCallBack
void ScanCallBack(const champion_nav_msgs::ChampionNavLaserScanPtr& scan_msg)
{
//转换到矫正需要的数据
ros::Time startTime, endTime;
startTime = scan_msg->header.stamp;
champion_nav_msgs::ChampionNavLaserScan laserScanMsg = *scan_msg;
//得到最终点的时间
int beamNum = laserScanMsg.ranges.size();
endTime = startTime + ros::Duration(laserScanMsg.time_increment * beamNum);
// 将数据复制出来
std::vector<double> angles, ranges;
for(int i = beamNum - 1; i > 0; i--)
{
double lidar_dist = laserScanMsg.ranges[i];
double lidar_angle = laserScanMsg.angles[i];
if(lidar_dist < 0.05 || std::isnan(lidar_dist) || std::isinf(lidar_dist))
lidar_dist = 0.0;
ranges.push_back(lidar_dist);
angles.push_back(lidar_angle);
}
//转换为pcl::pointcloud for visuailization
tf::Stamped<tf::Pose> visualPose;
if(!getLaserPose(visualPose, startTime, tf_))
{
ROS_WARN("Not visualPose, can not Calib");
return;
}
double visualYaw = tf::getYaw(visualPose.getRotation());
visual_cloud_.clear();
for(int i = 0; i < ranges.size();i++)
{
if(ranges[i] < 0.05 || std::isnan(ranges[i]) || std::isinf(ranges[i]))
continue;
double x = ranges[i] * cos(angles[i]);
double y = ranges[i] * sin(angles[i]);
pcl::PointXYZRGB pt;
pt.x = x * cos(visualYaw) - y * sin(visualYaw) + visualPose.getOrigin().getX();
pt.y = x * sin(visualYaw) + y * cos(visualYaw) + visualPose.getOrigin().getY();
pt.z = 1.0;
// pack r/g/b into rgb
unsigned char r = 255, g = 0, b = 0; //red color
unsigned int rgb = ((unsigned int)r << 16 | (unsigned int)g << 8 | (unsigned int)b);
pt.rgb = *reinterpret_cast<float*>(&rgb);
visual_cloud_.push_back(pt);
}
std::cout << std::endl;
//进行矫正
Lidar_Calibration(ranges, angles,
startTime,
endTime,
tf_);
//转换为pcl::pointcloud for visuailization
for(int i = 0; i < ranges.size();i++)
{
if(ranges[i] < 0.05 || std::isnan(ranges[i]) || std::isinf(ranges[i]))
continue;
double x = ranges[i] * cos(angles[i]);
double y = ranges[i] * sin(angles[i]);
pcl::PointXYZRGB pt;
pt.x = x * cos(visualYaw) - y * sin(visualYaw) + visualPose.getOrigin().getX();
pt.y = x * sin(visualYaw) + y * cos(visualYaw) + visualPose.getOrigin().getY();
pt.z = 1.0;
unsigned char r = 0, g = 255, b = 0; // green color
unsigned int rgb = ((unsigned int)r << 16 | (unsigned int)g << 8 | (unsigned int)b);
pt.rgb = *reinterpret_cast<float*>(&rgb);
visual_cloud_.push_back(pt);
}
//进行显示
g_PointCloudView.showCloud(visual_cloud_.makeShared());
}
3.5.6 获取机器人某时刻在里程计坐标系下的位姿
/**
* @name getLaserPose()
* @brief 得到dt时刻激光雷达在odom坐标系的位姿
* @param odom_pos 机器人的位姿
* @param dt dt时刻
* @param tf_
*/
bool getLaserPose(tf::Stamped<tf::Pose> &odom_pose,
ros::Time dt,
tf::TransformListener * tf_)
{
odom_pose.setIdentity();
tf::Stamped < tf::Pose > robot_pose;
robot_pose.setIdentity();
robot_pose.frame_id_ = "base_laser";
robot_pose.stamp_ = dt; //设置为ros::Time()表示返回最近的转换关系
// get the global pose of the robot
try
{
if(!tf_->waitForTransform("/odom", "/base_laser", dt, ros::Duration(0.5))) // 0.15s 的时间可以修改
{
ROS_ERROR("LidarMotion-Can not Wait Transform()");
return false;
}
tf_->transformPose("/odom", robot_pose, odom_pose);
}
catch (tf::LookupException& ex)
{
ROS_ERROR("LidarMotion: No Transform available Error looking up robot pose: %s\n", ex.what());
return false;
}
catch (tf::ConnectivityException& ex)
{
ROS_ERROR("LidarMotion: Connectivity Error looking up robot pose: %s\n", ex.what());
return false;
}
catch (tf::ExtrapolationException& ex)
{
ROS_ERROR("LidarMotion: Extrapolation Error looking up robot pose: %s\n", ex.what());
return false;
}
return true;
}
3.5.7 对激光雷达数据进行分段
/**
* @name Lidar_Calibration()
* @brief 激光雷达数据 分段线性进行差值 分段的周期为5ms
* @param ranges 激光束的距离值集合
* @param angle 激光束的角度值集合
* @param startTime 第一束激光的时间戳
* @param endTime 最后一束激光的时间戳
* @param *tf_
*/
void Lidar_Calibration(std::vector<double>& ranges,
std::vector<double>& angles,
ros::Time startTime,
ros::Time endTime,
tf::TransformListener * tf_)
{
// 统计激光束的数量
int beamNumber = ranges.size();
if(beamNumber != angles.size())
{
ROS_ERROR("Error:ranges not match to the angles");
return;
}
// 5ms来进行分段
int interpolation_time_duration = 5 * 1000;
tf::Stamped<tf::Pose> frame_start_pose;
tf::Stamped<tf::Pose> frame_mid_pose;
tf::Stamped<tf::Pose> frame_base_pose;
tf::Stamped<tf::Pose> frame_end_pose;
// 起始时间，单位为us
double start_time = startTime.toSec() * 1000 * 1000;
double end_time = endTime.toSec() * 1000 * 1000;
double time_inc = (end_time - start_time) / beamNumber; // 每束激光数据的时间间隔
// 当前插值的段的起始坐标
int start_index = 0;
// 起始点的位姿，这里要得到起始点位置的原因是：起始点就是我们的base_pose
// 所有的激光点的基准位姿都会改成base_pose
// ROS_INFO("get start pose");
if(!getLaserPose(frame_start_pose, ros::Time(start_time /1000000.0), tf_))
{
ROS_WARN("Not Start Pose,Can not Calib");
return ;
}
if(!getLaserPose(frame_end_pose,ros::Time(end_time / 1000000.0),tf_))
{
ROS_WARN("Not End Pose, Can not Calib");
return ;
}
int cnt = 0;
//基准坐标就是第一个位姿的坐标
frame_base_pose = frame_start_pose;
for(int i = 0; i < beamNumber; i++)
{
//分段线性,时间段的大小为interpolation_time_duration
double mid_time = start_time + time_inc * (i - start_index);
if(mid_time - start_time > interpolation_time_duration || (i == beamNumber - 1))
{
cnt++;
if(!getLaserPose(frame_mid_pose, ros::Time(mid_time/1000000.0), tf_))
{
ROS_ERROR("Mid %d Pose Error", cnt);
return ;
}
//对当前的起点和终点进行插值
//interpolation_time_duration中间有多少个点.
int interp_count = i - start_index + 1;
Lidar_MotionCalibration(frame_base_pose,
frame_start_pose,
frame_mid_pose,
ranges,
angles,
start_index,
interp_count);
//更新时间
start_time = mid_time;
start_index = i;
frame_start_pose = frame_mid_pose;
}
}
}
3.5.8 对分段数据插值后进行去畸变
/**
* @brief Lidar_MotionCalibration
* 激光雷达运动畸变去除分段函数;
* 在此分段函数中，认为机器人是匀速运动；
* @param frame_base_pose 标定完毕之后的基准坐标系
* @param frame_start_pose 本分段第一个激光点对应的位姿
* @param frame_end_pose 本分段最后一个激光点对应的位姿
* @param ranges 激光数据－－距离
* @param angles 激光数据－－角度
* @param startIndex 本分段第一个激光点在激光帧中的下标
* @param beam_number 本分段的激光点数量
*/
void Lidar_MotionCalibration(
tf::Stamped<tf::Pose> frame_base_pose,
tf::Stamped<tf::Pose> frame_start_pose,
tf::Stamped<tf::Pose> frame_end_pose,
std::vector<double>& ranges,
std::vector<double>& angles,
int startIndex,
int& beam_number)
{
//每个位姿进行线性插值时的步长
double beam_step = 1.0 / (beam_number - 1);
//机器人的起始角度 和 最终角度
tf::Quaternion start_angle_q = frame_start_pose.getRotation();
tf::Quaternion end_angle_q = frame_end_pose.getRotation();
//转换到弧度
double start_angle_r = tf::getYaw(start_angle_q);
double base_angle_r = tf::getYaw(end_angle_q);
//机器人的起始位姿
tf::Vector3 start_pos = frame_start_pose.getOrigin();
start_pos.setZ(0);
//最终位姿
tf::Vector3 end_pos = frame_end_pose.getOrigin();
end_pos.setZ(0);
//基础坐标系
tf::Vector3 base_pos = frame_base_pose.getOrigin();
base_pos.setZ(0);
double mid_angle;
tf::Vector3 mid_pos;
tf::Vector3 mid_point;
double lidar_angle, lidar_dist;
//插值计算每个点对应的位姿
for(int i = 0; i< beam_number; i++)
{
//角度插值
mid_angle = tf::getYaw(start_angle_q.slerp(end_angle_q, beam_step * i));
//线性插值
mid_pos = start_pos.lerp(end_pos, beam_step * i);
//得到激光点在odom 坐标系中的坐标
double tmp_angle;
//如果range不等于无穷,则需要进行矫正
if( tfFuzzyZero(ranges[startIndex + i]) == false )
{
//计算对应的激光点在odom坐标系中的坐标
//得到这帧激光束距离和夹角
lidar_dist = ranges[startIndex+i];
lidar_angle = angles[startIndex+i];
//激光雷达坐标系下的坐标
double laser_x, laser_y;
laser_x = lidar_dist * cos(lidar_angle);
laser_y = lidar_dist * sin(lidar_angle);
//里程计坐标系下的坐标
double odom_x, odom_y;
odom_x = laser_x * cos(mid_angle) - laser_y * sin(mid_angle) + mid_pos.x();
odom_y = laser_x * sin(mid_angle) + laser_y * cos(mid_angle) + mid_pos.y();
//转换到类型中去
mid_point.setValue(odom_x, odom_y, 0);
//把在odom坐标系中的激光数据点 转换到 基础坐标系
double x0, y0, a0, s, c;
x0 = base_pos.x();
y0 = base_pos.y();
a0 = base_angle_r;
s = sin(a0);
c = cos(a0);
/*
* 把base转换到odom 为[c -s x0
* s c y0
* 0 0 1]
* 把odom转换到base为 [c s -x0*c-y0*s
* -s c x0*s-y0*c
* 0 0 1]
*/
double tmp_x,tmp_y;
tmp_x = mid_point.x()*c + mid_point.y()*s - x0*c - y0*s;
tmp_y = -mid_point.x()*s + mid_point.y()*c + x0*s - y0*c;
mid_point.setValue(tmp_x, tmp_y, 0);
//然后计算以起始坐标为起点的 dist angle
double dx,dy;
dx = (mid_point.x());
dy = (mid_point.y());
lidar_dist = sqrt(dx*dx + dy*dy);
lidar_angle = atan2(dy, dx);
//激光雷达被矫正
ranges[startIndex+i] = lidar_dist;
angles[startIndex+i] = lidar_angle; //基础坐标系下激光的距离和角度值
}
//如果等于无穷,则随便计算一下角度
else
{
//激光角度
lidar_angle = angles[startIndex+i];
//里程计坐标系的角度
tmp_angle = mid_angle + lidar_angle;
tmp_angle = tfNormalizeAngle(tmp_angle);
//如果数据非法 则只需要设置角度就可以了。把角度换算成start_pos坐标系内的角度
lidar_angle = tfNormalizeAngle(tmp_angle - start_angle_r);
angles[startIndex+i] = lidar_angle;
}
}
}
4 参考
- 激光SLAM理论与实践
- VICP:Velocity updating Iterative Closest Point Algorithm
- LOAM:Lidar Odometry and Mapping in real-time
- https://github.com/liuzhongyin1109/laser_undistortion