Optimization-based Localization And Mapping

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This is an introductory course to Optimization-based Localization And Mapping.

Two common problems that fall under this umbrella are visual odometry (VO) and  Simultaneous Localization And Mapping (SLAM). While the name of the former suggest that a camera is used it is often implemented based on LiDAR data. In same cases it is refer to LO or LOAM to emphasise the existence of a map as part of the solution. 

In addition to exteroceptive sensors such as cameras and LiDAR, data from intertial measurement devices such as accelerometers and gyros (typically packaged in an IMU) are often used. VO that makes use of intertial information is often referred to as VIO.

 

Target audience and Prerequisites

The target audience for the course are doctoral students in the general area of autonomous systems. 

No prior knowledge in robotics, visual odometry or SLAM is need, but will be helpful. Ability to program is required.

 

Teachers

  • The course is taught by Dr. Thien-Minh Nguyen who also developed the course and its material.
  • The examiner is Patric Jensfelt.

 

Intended learning outcomes (ILOs)

After completing the course the student 

  1. can explain the basic principles and core components of an optimisation-based localization and mapping (OBLAM) system and
  2. has the ability to critically assess the strength and weaknesses of OBLAM solutions.
  3. can describe obervation models of common sensors.

 

Course code

This course does not have its own course code. It is a "module" that plus into one of the Topics in Robotics course, DD3353, DD3354 or DD3355 worth 3, 6 or 9hp respectively. It is up to the student to decide, for example influenced by having other modules that you want to report, which of the Topics in Robotics courses to report the result into and to notify the examiner about the corresponding Topics in Robotic course about it.

 

 

Part 1 (6hp)

This part of the course is expected to last from November, 2022 to late January, 2023.

Sessions

0. Course overview and introduction
1. Maximum A Posteriori Optimization
2. Observation Models - Part 1: Preliminary, Lidar, Camera
3. Observation Models - Part 2: IMU + Technical tutorial: Running & Benchmarking SLAM
4. Discussion on Assignments

5. Review

Examination

The examination consist of active participation in the sessions and completing the assignments

  1. Benchmarking public VINS/LIOM methods and datasets
  2. Analyse and Report on public code snippets
  3. Loop closure and Pose-Graph Optimization Exercise
  4. IMU-propagation for deskewing Lidar.

 

Part 2 (3hp)

This part of the course is optional. It consists of a self-defined project that is worth 3hp. The project can be carried out individual or in pairs. If done in pairs you need to make it clear that both have contributed to the work and learning.

The project should connect to the course content but it can be both going deeper into one of the topics already covered in the course or broadening it. For some students you might be able to connect it to your research.

  1. Final project proposal (pdf)
  2. Final project presentation

Course Summary:

Date Details Due