std::vector to Eigen::MatrixXd Eigen

Question

I would like to know whether is there an easier way to solve my problem rather than use a for loop. So here is the situation:

In general, I would like to gather data

Answer 1

This solution should work:

Eigen::MatrixXd sensor_input = Eigen::MatrixXd::Map(sensor_input_vector[0].data(),
 3, sensor_input_vector.size());

Since your output will be a matrix of 3 x N (N is the number of 3D vectors), you could use the Map function of Eigen::Matrix3Xd too:

Eigen::Matrix3Xd sensor_input = Eigen::Matrix3Xd::Map(sensor_input_vector[0].data(),
 3, sensor_input_vector.size());

Answer 2

Short answer: You need to write (after making sure that your input is not empty):

sensor_input = Eigen::Map<Eigen::MatrixXd>(sensor_input_vector[0].data(),3,sensor_input_vector.size());

The reason is that Eigen::Map expects a pointer to the underlying Scalar type (in this case double*), whereas std::vector::data() returns a pointer to the first element inside the vector (i.e., an Eigen::Vector3d*).

Now sensor_input_vector[0].data() will give you a pointer to the first (Vector3d) entry of the first element of your std::vector. Alternatively, you could reinterpret_cast like so:

sensor_input = Eigen::Map<Eigen::MatrixXd>(reinterpret_cast<double*>(sensor_input_vector.data()),3,sensor_input_vector.size());

In many cases you can actually avoid copying the data to a Eigen::MatrixXd but instead directly work with the Eigen::Map, and instead of MatrixXd you can use Matrix3Xd to express that it is known at compile time that there are exactly 3 rows:

// creating an Eigen::Map has O(1) cost
Eigen::Map<Eigen::Matrix3Xd> sensor_input_mapped(sensor_input_vector[0].data(),3,sensor_input_vector.size());
// use sensor_input_mapped, the same way you used sensor_input before

You need to make sure that the underlying std::vector will not get re-allocated while sensor_input_mapped is used. Also, changing individual elements of the std::vector will change them in the Map and vice versa.