*Nov*

Last week, I explained why typical distributed gradient descent leads to inexact solution, even though the convergence rate is linear (in log scale). There is a very important paper that shows that we can in fact have exact solution by incorporating the previous gradient. This algorithm is called...

*Oct*

What DARPA wants: Independent derivation of action and tasks within a team with limited or degraded communication is critical but not explored. DARPA desires efficient, bundled perception, localization, planning, and control algorithms for small robots to move rapidly and accomplish a multi-robo...

*Oct*

We studied the cooperative localization algorithm in multirobot systems before. However, when we want to apply the algorithm in the realistic multirobot system, a lot of realistic issues need to be addressed. One of the critical issues is the assumption in communication. Wireless Communication and...

*Aug*

We simulate the BOEM algorithm against two other offline algorithms. Since these two algorithms requires a long time, we simulated them with sliding windows. In the first scenario, the block size of BOEM started with 9 sec, and grows geometrically. The sliding windows for both EM and optimizati...

*Jul*

We simulation these SLAM algorithms with increasing duration. We can see that the estimation accuracy is comparable among 3 SLAM algorithms, but the processing times are different. The processing time is basically dominated by the optimization problem. The bigger the optimization problem, the long...

*Jun*

Following up with Alexie's post last week, we furthermore show that the simulation of BOEM SLAM algorithm for 1000 sec. The result is the average of 5 trials. The computation times for other algorithm, including optimization based and the EM SLAM, are too large for 1000 sec, but we have the comparis...

*Jun*

We compare 3 different SLAM algorithms on EuRoC datasets. As for estimation accuracy, we can see that all SLAM algorithms can improve the accuracy upon the visual-inertial odometry VIO output. The optimization SLAM has the most consistent result, while all SLAM algorithms have comparable perform...

*May*

The optimization based SLAM formulate the optimization problem with two kinds of constraints. First, the pre-integrated IMU error establishes the constraints between two spatial states, or two poses. The other one is the reprojection error that links a spatial state and a landmark. In the optimizati...

*Apr*

We are now able to demonstrate that the performances of EM-based SLAM algorithms are comparable to that of optimization-based. However, it is not easy to observe that same thing in the real-world dataset. simulation result The first challenge is whether to use IMU data or to only use the output...

*Apr*

In our previous paper, we investigate the consensus problem with von Mises distribution. We want to extend our analysis to more general distributions, in particular the von Mises-Fisher (vMF) distribution over hyperspheres. In fact, vMF distributions are used in some machine learning algorithms. I...