# Tag Info

3

I don't know if this is mathematically sound or not, but it's given me good results in practice. What I've done in this situation personally, when I expect/know that there is a bias, is to include the bias as a state in my state space representation of the system. For example, if I have a system to track position, I might write the state space models like: ...

3

First off, it doesn't sound like you're actually doing SLAM. You didn't mention an exteroceptive sensor (e.g., laser, camera) that actually maps the environment. With just an IMU, you are doing localization, or more specifically, dead-reckoning. With just an IMU, there is no way to actually implement pose-graph SLAM in its usual formulation. That being said,...

2

Suppose you have three measurements (1, 2, and 3) and four landmarks (a, b, c, d). The joint compatibility is a measure of how well a subset of the measurements associates with a subset of the landmarks. For example, what is the joint compatibility of (1b, 2d, 3c)? First we construct the implicit measurement functions $f_{ij_i}$ for each correspondence ($... 1 The value$S^k\$ (innovation variance) needs to be calculated for all landmarks, but the subsequent update steps (post line 16 --- after argmax), need be applied to all of the map, given the landmark update that was selected on line 16 --- the argmax. FYI, argmax searches over the list of landmarks for the landmark maximizing the equation given. It selects ...

1

This is not the answer of your question but ORB is a feature detection algorithm. Feature detection and tracking are different. Once you found a feature it is better to track them by a feature tracking algorithm such as KLT. You can also track the features by feature matching algorithms but it is not very efficient. Since features come with their own ...

1

In EKF slam generally we include landmarks which can be marked as points: Corners, columns, trees etc. The uncertainties for landmarks are calculated by assuming that those are point landmarks. (1) Since it's not straightforward to include a line in EKF SLAM you need to come up with a solution to convert them to feature points. Using the center point of the ...

1

It should be processed features. Extracting features from raw data is usually called as front-end in SLAM. The easiest forms of features in cased of 2D LiDAR are corners, edges, and lines. You can run RANSAC algorithm to find line segments. Corners are found by intersection of lines and edges are ends of lines. Corners should be enough for a uni project. ...

Only top voted, non community-wiki answers of a minimum length are eligible