Incremental Smoothing and Mapping with iSAM2 and Factor Graphs
Discover the world of iSAM2, an incremental smoothing and mapping technique using the Bayes Tree. Learn about Factor Graphs and their role in optimizing variable assignments. Dive into the intricate details of matrix vs. graph measurements, inference and elimination algorithms, and more in the realm of robotics and navigation.
Download Presentation
Please find below an Image/Link to download the presentation.
The content on the website is provided AS IS for your information and personal use only. It may not be sold, licensed, or shared on other websites without obtaining consent from the author. If you encounter any issues during the download, it is possible that the publisher has removed the file from their server.
You are allowed to download the files provided on this website for personal or commercial use, subject to the condition that they are used lawfully. All files are the property of their respective owners.
The content on the website is provided AS IS for your information and personal use only. It may not be sold, licensed, or shared on other websites without obtaining consent from the author.
E N D
Presentation Transcript
iSAM2: Incremental Smoothing and Mapping Using the Bayes Tree Michael Kaess, Hordur Johannsson, Richard Roberts, Viorela Ila, John Leonard, and Frank Dellaert
iSAM (Kaess et al., TRO 08) Solving a growing system: Exact/batch (quickly gets expensive) Approximations Incremental Smoothing and Mapping (iSAM)
iSAM (Kaess et al., TRO 08) Key Idea: Append to existing matrix factorization. Repair using Givens rotations. Periodic batch steps for Relinearization. Variable reordering (to keep sparsity)
Matrix vs. Graph Measurement Jacobian A Factor Graph Information Matrix ATA Markov Random Field R ? Square Root Inf. Matrix
Factor Graph(Kschischang et al., 2001) A bipartite graph Factor nodes Variable nodes Edges are always between factor nodes and variables nodes.
The goal is to find the variable assignment that maximizes the function before:
Inference and Elimination Inference Converting the factor graph to a Bayes net using the elimination algorithm Elimination is done by using bipartite elimination game
After eliminating all variables, the Bayes net density is defined by the product of the conditionals produced at each step:
Bayes Tree Definition A directed graph Similar to Bayes net as it encodes a factored probability density. Includes one conditional density per node with separator Sk Frontal variables Fk as the remaining variables
The iSAM2 Algorithm
