Lidar SLAM for DNV GL Fuel Fighter

Background DNV GL Fuel Fighter is a student project working with the goal of making the world's most energy efficient car. Since 2008 we have competed in Shell Eco Marathon, the world’s leading student competition on energy efficiency having over 2000 students competing every year. The team was started as a master project among a group of friends, and has developed into one of the best student teams in the competition. Some of our recent accomplishments involve 2nd place Urban Concept Battery Electric 2018, 1st place Communication Award 2018, 1st place Design Award 2019.

In 2018, Shell held the first autonomous competition in Shell Eco Marathon. Since then we have began developing our system and our goal is to compete in 2021. In 2019 the autonomous competition consisted of the challenges in the picture below, increasing in difficulty as you pass each one. To achieve this it is vital to have a virtual mapping of the surroundings, so the car can be able to see the track and potential obstacles. Ouster, as one of our main sponsors, is frequently helping us by answering questions and giving tips on how to get the most out of their OS1 LiDAR.

Scope The overall goal of this project is to make a virtual mapping of the challenges set by Shell to try and win the autonomous competition. As a master student in DNV GL Fuel Fighter you will primarily be a part of the autonomous group, but you will also work closely with the rest of the team consisting of students from several different studies and nationalities. The project is intended for a single student, but can also be solved as a collaboration of two. It is prefered to combine the 5th year project and master thesis, to be able to make a working system for the competition.

Proposed tasks for the 5th year project A dilemma in robotics is that in order to make a map of the robot’s surroundings, its position is needed, but for finding your position, you need a map. Simultaneous Localization and Mapping (SLAM) solves this problem. It finds a map of an unknown environment at the same time as finding its own position within the map. Your task is to use our Ouster OS1 LiDAR combined with IMU - data to find an accurate position and mapping with loop closure allowing us to safely navigate through the track. Suggestions on specific tasks:

Project topic proposal 1

Research visual SLAM for LiDAR.

Deep learning research for cameras is outpacing lidar research. LiDAR data has incredible benefits - rich spatial information and lighting sensing to name a couple - but it lacks the raw resolution and efficient array structure of camera images, and 3D point clouds are still more difficult to encode in a neural net or process with hardware acceleration. OS1 outputs fixed resolution depth images, signal images, and ambient images in real time, all without a camera as seen in the picture below. The idea is that you now have a 360 degree camera with depth information. Your task is to do research on this topic and come up with potential solutions.

Project topic proposal 2

Research SLAM - algorithms for use on a racetrack

There are several open source LiDAR SLAM - algorithms for point clouds with IMU available. What makes them good in their given environment? Research different algorithms and theories needed to make the best custom SLAM for the purpose of the autonomous competition in Shell Eco Marathon. An example of a choice needed to be made is whether to use feature-extraction or feature-free representation.

Proposed tasks for the master thesis Master thesis proposal 1

Implement visual SLAM for LiDAR.

Based on your research in project topic proposal 1, implement visual SLAM for LiDAR. You can either adapt Visual SLAM to be used in this setting or make a new one.

Master thesis proposal 2

Implement SLAM - algorithm for use on a racetrack

Implement your own version of SLAM based on your research in project topic proposal 2. This could for example be based on filters, global optimization or convolutional neural networks. You can decide yourself if you go for a feature based or a feature free approach.

References ● Pacala, A (2018). “The Lidar IS Camera”. Ouster Blog. ● Selby, W (2020). “Developing an autonomous driving ML algorithm using OS1 intensity data”.

Ouster Blog.