This crate implements a navigation filter based on an Error State Kalman Filter.
This crate supports no_std environments, but few optimizations to the
mathematics have been attempted to optimize for no_std.
An Error State Kalman Filter is a navigation filter based on regular Kalman filters, more specifically Extended Kalman Filters, that model the "error state" of the system instead of modelling the movement of the system explicitly.
The navigation filter is used to track position, velocity and orientation
of an object which is sensing its state through an Inertial Measurement Unit
(IMU) and some means
of observing the true state of the filter such as GPS, LIDAR or visual odometry.
use eskf;
use nalgebra::{Vector3, Point3};
use std::time::Duration;
// Create a default filter, modelling a perfect IMU without drift
let mut filter = eskf::Builder::new().build();
// Read measurements from IMU
let imu_acceleration = Vector3::new(0.0, 0.0, -9.81);
let imu_rotation = Vector3::zeros();
// Tell the filter what we just measured
filter.predict(imu_acceleration, imu_rotation, Duration::from_millis(1000));
// Check the new state of the filter
// filter.position or filter.velocity...
// ...
// After some time we get an observation of the actual state
filter.observe_position(
Point3::new(0.0, 0.0, 0.0),
eskf::ESKF::variance_from_element(0.1))
.expect("Filter update failed");
// Since we have supplied an observation of the actual state of the filter the states have now
// been updated. The uncertainty of the filter is also updated to reflect this new information.