In external sensor API, a measurement can be added to a list. However, timestamp isn't checked which can lead to doubled measurement in list.
These doubled measurement can make the interpolation fail or decrease performances.

The tab key can be used to propose the different launch files for a specific package.

* Offset transform was inverted in computations
* Documentation is fixed
* Rename GpsFrameId to MapFrameId and change default value to map
* [ROS2] GPS reference frame is usually normalized (ECEF, ENU, etc.) while SLAM reference frame (i.e. odom) is local : e.g., first LiDAR sensor pose. It is more common to define the offset as the transform from the normalized reference to odom in the tree
* [ROS2] The map->odom transform is updated not to be static as odom is expected to move in time

* Frame could be linked with TF tree as well...

The calibration TF published was badly set (wrong frame ID)

The frame ID of the ext pose message is the frame in which the pose is represented, not the tracked one (e.g. "odom" not "ins") so we need to add a frame ID "ins" to get the calibration. User will need to set ins in his tf tree.

540812 authored 1 week ago and Tong Fu committed 1 week ago

This commit solves Eigen alignement issues at runtime (not deterministic segfault in some installs). This avoids to put Eigen macro on each class.
See here for details : https://eigen.tuxfamily.org/dox-devel/group__TopicUnalignedArrayAssert.html?_sm_au_=iHVrVkPbL7fLQPMNWBBQ8K3QcpW6W

Some OpenMP versions don't allow to process loops on iterators and only allow to loop on int.
This change solves build bugs in some installations

* Nbthread was not declared in aggregation node
* A parameter is added

Previously, lidar conversion node considers the pointcloud from sensor is always
assembled in the order. So a frame duration is computed by time of the first and
the last point.

Sometimes pointcloud from the sensor driver can have an abnormal time order
due to the packet loss. Now the duration is computed by the min and max value
of time in a pointcloud.