sollimann / cleanit Goto Github PK

Thanks for the covariance computations at https://github.com/Sollimann/CleanIt/blob/main/autonomy/src/slam/README.md !

However, in the definition of $F_p = \frac{\delta f}{\delta x, \delta y, \delta\theta}$, it seems to me the equation for $\frac{\delta f}{\delta\theta}$ should have $\Delta s$ in absolute values (on both first and second row). I can't figure out mathematically why it should be so, but practically, if I use the signed $\Delta s$, the covariance decreases when the robot reverses, which is probably not what you want.

Do you also observe this behavior?

The architecture can be improved by using e.g NATS for messaging between services, while using gRPC as a forward facing API (facade) for the user. This should improve the design.

Could also look into using actix actor framework instead of NATS

Consider using tonic gRPC as forward facing API, while using message-io for internal communication / networking