ptudes-lab 0.0.7
Ptudes lab: odometry, SLAM and visualization experiments
License: MIT
Requires Python: <3.12,>=3.8

==================================================================
P(oint) (e)Tudes Lab: Lidar odometry, SLAM and visualization tools
==================================================================

This is a playground of various experiments with SLAM, mapping and visualization
of lidar point clouds. (``Ptudes`` name is an interplay of ``P(oint) (e)Tudes``,
derived from `Etude`_)

.. _Etude: https://en.wikipedia.org/wiki/%C3%89tude

It's heavily using and relying on Ouster sensor lidar data, Ouster SDK, public
datasets that contain Ouster lidar data and lidar odometry poses obtained from
`KISS-ICP`_ package.

Everything in ``ptudes-lab`` package works for multi Python (3.8 - 3.11) and
multi OS (Linux, MacOS, Windows).

Table of contents:

.. contents::
   :local:
   :depth: 1

.. _flyby-viz:

Flyby 3d visualizations of lidar data with poses
-------------------------------------------------

Review the registered point cloud map using the per scan poses of the
odometry/slam pipeline with deskewing and point coloring by ``REFLECTIVITY``,
``NEAR_IR``, ``SIGNAL`` and ``RANGE`` channels (channels availability depends on
the UDP Lidar Profile of the data).

.. figure:: https://github.com/bexcite/ptudes-lab/raw/main/docs/images/flyby.png

Pre-requisite:
~~~~~~~~~~~~~~

0. Installation
````````````````

You can install ``ptudes-lab`` using Pip from the PyPi
using::

    pip install ptudes-lab

or you can install it in editable mode if you plan to modify the code (or want
to use not yet released features)::

    git clone https://github.com/bexcite/ptudes-lab.git
    cd ptudes-lab
    pip install -e .

NOTE: Don't forget to use `venv` or any other means of controlling the Python
environments, they always save a lot of time later down the road.

1. Get Ouster sensor lidar data in a ``.pcap/.bag`` format
```````````````````````````````````````````````````````````

You can download a sample data from the `official sensor docs`_:

Or you can record it from the sensor if you have one, using ``ouster-sdk/cli``::

    ouster-cli source <MY_SENSOR_IP> record

2. Get the lidar scans poses in kitti format or NC GT format
`````````````````````````````````````````````````````````````

You `KISS-ICP`_ pose outputs in KITTI format directly running the official
``kiss_icp_pipeline`` on the previously obtained Ouster ``.pcap`` data using::

    kiss_icp_pipeline --deskew ./OS-0-128_v3.0.1_1024x10.pcap

You can use any pose source with ``--kitti-poses`` in the command ``ptudes
flyby`` below and not necessarily ``KISS-ICP`` output. For example it can be
the result of some post-processing step (smoothing, loop closure, fusion with
other sensors etc) the only requirement is that the number of poses should be
the same as the number of scans in the ``.pcap/.bag`` file.

Alternatively you can run ``ptudes ekf-bench ouster`` command with
``--save-nc-gt-poses poses.csv`` to get the trajectory in Newer College Dataset
Ground Truth format (*NC GT*), which has a much better utility value since it
contains timestamp per pose and can be used to calculate ATE between
trajectories.

.. _official sensor docs: https://static.ouster.dev/sensor-docs/#sample-data
.. _KISS-ICP: https://github.com/PRBonn/kiss-icp

How to run:
~~~~~~~~~~~

Once you have Ouster sensor ``.pcap/.bag`` data and poses per every scan in
KITTI format (or NC GT format) you can run ``ptudes flyby`` command as::

    ptudes flyby ./OS-0-128_v3.0.1_1024x10.pcap --kitti-poses ./OS-0-128_v3.0.1_poses_kitti.txt

or for example using ``.bag`` from `Newer College`_ dataset and the NC GT ground truth data::

    ptudes flyby ./newer-college/2021-ouster-os0-128-alphasense/collection1/2021-07-01-10-37-38-quad-easy.bag \
        --meta ./newer-college/2021-ouster-os0-128-alphasense/beam_intrinsics_os0-128.json \
        --nc-gt-poses ./newer-college/2021-ouster-os0-128-alphasense/collection1/ground_truth/gt-nc-quad-easy.csv \
        --start-scan 20 \
        --end-scan 50

Use ``--help`` to see more options like ``--start-scan/--end-scan`` to view only
a specific range of scans.

Some useful keyboard shortcuts for ``flyby`` command:

==============  =============================================================
Key             Action
==============  =============================================================
``SPACE``       Stop/Start flying
``>``           Increase/decrease flying speed
``8``           Toggle poses/trajectory view
``k / K``       Cycle point cloud coloring mode of accumulated clouds or map
``g / G``       Cycle point cloud color palette of accumulated clouds or map
``j / J``       Increase/decrease point size of accumulated clouds or map
==============  =============================================================

.. _Newer College: https://ori-drs.github.io/newer-college-dataset/


ROS bags visualizations of raw lidar data
------------------------------------------------------

Ouster sensors produce raw ``lidar_packets/imu_packets`` data in corresponding
ROS topics. To view the point cloud from such raw packets BAGs without spinning a
ROS and installing all drivers one can use ``ptudes viz`` command.

    Not tested with ROS2 bags:(
    
    I wasn't been able to locate the ROS2 bag with raw Ouster ``lidar_packets``,
    so if you by any chance have such a ROS2 bag that you can share with me I
    can make sure that both ROS1 and ROS2 bags working for the ``ptudes viz``
    command. (i.e. ``ptudes.bag.OusterRawBagSource`` packet source can work with
    ROS1/ROS2 bags)

For example to visualize `Newer College` dataset BAGS use::

    ptudes viz ./newer-college/2021-ouster-os0-128-alphasense/collection1/2021-07-01-10-37-38-quad-easy.bag \
        --meta ./newer-college/2021-ouster-os0-128-alphasense/beam_intrinsics_os0-128.json

and it will open:

.. figure:: https://github.com/bexcite/ptudes-lab/raw/main/docs/images/viz_nc_bag.png


Since the underlying Viz is the `PointViz`_ shipped with Ouster SDK the full
list of keyboard shortcuts can be found `here`_

.. _PointViz: https://static.ouster.dev/sdk-docs/python/viz/index.html
.. _here: https://static.ouster.dev/sdk-docs/sample-data.html#id1


Ouster Lidar/IMUs + KissICP + trajectory smoothing (Error-State EKF)
---------------------------------------------------------------------

**NOTE:** Refer to the blog post `Lidar odometry smoothing using ES EKF and KissICP for
Ouster sensors with IMUs
<https://capsulesbot.com/blog/2024/02/05/esekf-smoothing-ouster-lidar-with-imu-using-kiss.html>`_
for ES EKF formulation details and experiments described in details.

Ouster Lidar raw ``.pcap/.bag`` recordings almost always come with
**imu_packets** inside which may be used to get better trajectories estimation
on some tricky cases, like tunnels with less features, fast platform movements
or lower resolution sensors. (though it's not universally better and need to be
used with caution).

``ptudes ekf-bench`` CLI has various tools with ES EKF implementation that uses
the Ouster **imu_packets** together with KissICP.

ES EKF as a smoothing filter for KissICP trajectories
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Use ``ptudes ekf-bench ouster`` command that can run on Ouster Lidar raw
recordings in ``.pcap/.bag`` and outputs smoothed KissICP poses that can be
compared with available ground truth automatically (in Newer College Dataset
format) by plotting trajectories together and calculating Average Trajectory
Error (ATE).

For example, result of the run on ``quad-easy.bag`` from the ``collection1`` of
NCD looks like this::

    ptudes ekf-bench ouster ./newer-college/2021-ouster-os0-128-alphasense/collection1/2021-07-01-10-37-38-quad-easy.bag \
      --gt-file ./newer-college/2021-ouster-os0-128-alphasense/collection1/ground_truth/gt-nc-quad-easy.csv \
      --save-nc-gt-poses quad-easy.csv \
      -p graphs

With graphs showing the smoothing in action:

.. figure:: https://github.com/bexcite/ptudes-lab/raw/main/docs/images/ekf_smoothing_ouster_easy.png

and KissICP adaptive threshold estimates per scan with a corresponding pose
innovation from ICP update:

.. figure:: https://github.com/bexcite/ptudes-lab/raw/main/docs/images/ekf_smoothing_sigma.png

To use ES EKF prediction as a KissICP initial guess, you can use
``--use-imu-prediction`` option with ``ptudes ekf-bench ouster``.

And then use ``ptudes ekf-bench cmp`` command to compare various trajectories::

    ptudes ekf-bench cmp ./newer-college/2021-ouster-os0-128-alphasense/collection1/ground_truth/gt-nc-quad-easy.csv \
      quad-easy.csv \
      quad-easy-imu-pred.csv \
      -p graphs --xy-plot

.. figure:: https://github.com/bexcite/ptudes-lab/raw/main/docs/images/ekf_smoothing_imu_pred_compare.png