Understanding SLAM Using Pose Graph Optimization | Autonomous Navigation, Part 3

This video provides some intuition around Pose Graph Optimization—a popular framework for solving the simultaneous localization and mapping (SLAM) problem in autonomous navigation.

We’ll cover why uncertainty in a vehicle’s sensors and state estimation makes building a map of the environment difficult and how pose graph optimization can deal with it. We’ll also briefly cover occupancy grid maps as one way to represent the environment model.

Additional Resources:

Watch the other videos in this series:
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[prandtlmayer]

Brian, this is possibly the best introductory video to pose graph I have seen so far

[adnanfahad]

I can see how much effort has been put into this video. Great explanation!!! Thanks a lot.

[xephyr]

I love you, Brian Douglas. You have been teaching me for so many years.

[ibadrather]

I did udacity Robotics Software Engineering Nanodegree. and studied a lot about SLAM. BUt this explanation beats everything. wow.

[alfascanerllc]

Very useful stuff, really academic and easy to comprehend at the same time. Thanks.

[talhayousuf]

Ah! Brian Douglas! I learnt all control systems concepts from you, by your videos I was able to integrate equations with real world, you are wonderful at teaching. Allah bless you

[bluecpp]

Thank you for these great introductory videos to the topic. 👍

[snackbite]

dude im very grateful for the resourceful explanation. thank you so much, this is what future of education will look like. bless you all who reads this xoxo

[mikeharville]

Awesome video, with excellent visual illustrations! However: It seems to imply that you create a loop closure link when you somehow know (from external means) that the robot is in the exact same place as before. My understanding is that what people actually do is identify matching features (or sets of features) between measurements at two _different_ but similar robot locations, estimate a relative pose between those that would satisfy the observed changes (e.g. image locations for visual features, or angles and distances for lidar features) in the matched features in the two measurements, and then add that relative pose as the new link in the graph. It would be good (maybe in a followup video) to go into that more, and also into how one actually optimizes the pose graph, which again would involve some matching/alignment between features or other measurements at linked poses.

[jiayonglau]

Thank you for your effort! Very underrated video!!

[orhirshfeld]

Always love to learn from you, you make it easy to learn

[apppurchaser]

Amazing man thank you so much for clean and concise explanations

[gennarofarina]

Great intro to the process, thank you!

[androclassic]

Thanks, best visualisation of PGO I have seen so far ! 👍👍

[alhdlakhfdqw]

thank you so much for your perfect video!

[MEETPATEL-utqg]

Invested a great effort to make an easygoing presentation.

[Craftinges]

Yeah the part I dont understand is the 10:53. How do you correct the previous stages? you divide the error in rotation, x, y between the last and first stage to 6 (number of total steps) and add it to all stages like averaging? or pecentage wise weighted averaging. Because if robot didnt move in x only moved in y direction in that step, error should be less or 0 in x direction? Also error is less in early stages. It increases as it goes if we assume its gaussian and constant so if we give credibiltiy to each stage then error weight should be increase like 1-2-3-4-5-6 so kind of 1/21 of error will be added to first, 2/21 of the error will be added to 2nd stage and so on?

[ayushpatel]

I had a doubt, without loop closure, will there not be error? Does this error remain in the system or is it rectified by other any means if the robot does not come back to its initial pose? In this case, will the error propogate?

[amaurypalacios]

Great video, excelent explanation, thanks.

[ma-xzof]

Amazing - thx for the very clear explanation