Outline:

• Overview of SLAM
• Learning-based methods for SLAM
• How do we handle the hard cases in SLAM? What are the challenges ahead?
• Bridging the gap between dataset validation and real-world system deployment

Factor graphs have become a popular tool for modeling robot perception problems. Not only can they model the bipartite relationship between sensor measurements and variables of interest for inference, but they have also been instrumental in devising novel inference algorithms that exploit the spatial and temporal structure inherent in these problems.

I will start with a brief history of these inference algorithms and relevant applications. I will then discuss open challenges in particular related to robustness from the inference perspective and discuss some recent steps towards more robust perception algorithms.

In this session, Prof. Michael Milford discusses five key questions and open research areas in the bio-inspired mapping, navigation, and SLAM area, linking into past and recent neuroscience and biological discoveries:

• The Loop Closure Question
• The 3D Question
• The Probabilistic Question
• The Multi-Scale Question
• The Behavioural Question

Prof. Michael also presents an objective take on opportunities and mysteries in the area that also recognizes the practical realities and requirements of modern-day SLAM applications.

Enabling centimeter-accuracy positioning and human-like scene understanding for autonomous vehicles and mobile devices, holds potentially huge implications for practical applications. Optimal fusion of visual and inertial sensors provides a popular means of navigating in 3D, in part because of their complementary sensing modalities and their reduced cost and size.

In this talk, I will present our recent research efforts on visual-inertial estimation and perception. I will first discuss the observability-based methodology for consistent state estimation in the context of simultaneous localization and mapping (SLAM) and visual-inertial navigation system (VINS), and then will highlight some of our recent results on visual-inertial estimation, including OpenVINS, inertial preintegration for graph-based VINS, robocentric visual-inertial odometry, Schmidt-EKF for visual-inertial SLAM with deep loop closures, visual-inertial moving object tracking and many others.