gradSLAM: Dense SLAM meets automatic differentiation

gradSLAM: An overview of the differentiable SLAM pipeline


The question of “representation” is central in the context of dense simultaneous localization and mapping (SLAM). Newer learning-based approaches have the potential to leverage data or task performance to directly inform the choice of representation. However, learning representations for SLAM has been an open question, because traditional SLAM systems are not end-to-end differentiable. In this work, we present gradSLAM, a differentiable computational graph take on SLAM. Leveraging the automatic differentiation capabilities of computational graphs, gradSLAM enables the design of SLAM systems that allow for gradient-based learning across each of their components, or the system as a whole. This is achieved by creating differentiable alternatives for each non-differentiable component in a typical dense SLAM system. Specifically, we demonstrate how to design differentiable trust-region optimizers, surface measurement and fusion schemes, as well as differentiate over rays, without sacrificing performance. This amalgamation of dense SLAM with computational graphs enables us to backprop all the way from 3D maps to 2D pixels, opening up new possibilities in gradient-based learning for SLAM.

In International Conference on Robotics and Automation
Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.
Click the Slides button above to demo Academic’s Markdown slides feature.

Supplementary notes can be added here, including code and math.

Krishna Murthy Jatavallabhula
Krishna Murthy Jatavallabhula
PhD Candidate

My research blends robotics, computer vision, graphics, and physics with deep learning.

Ganesh Iyer
Masters Student
Liam Paull
Assistant Professor

I lead the Montreal robotics and embodied AI lab. I am affiliated with Université de Montréal, Mila, and I hold a CIFAR AI chair.

comments powered by Disqus