navlie.utils.common

Collection of miscellaneous utility functions and classes.

Functions

associate_stamps(first_stamps, second_stamps)	Associate timestamps.
find_nearest_stamp_idx(stamps_list, stamp)	Find the index of the nearest stamp in stamps_list to stamp.
jacobian(fun, x[, step_size, method])	Compute the Jacobian of a function.
load_tum_trajectory(fpath)	Loads a TUM trajectory file into a list of SE3State objects.
monte_carlo(trial, num_trials[, num_jobs, ...])	Monte-Carlo experiment executor.
randvec(cov[, num_samples])	Produces a random zero-mean column vector with covariance given by cov
schedule_sequential_measurements(model_list, ...)	Schedules sequential measurements from a list of MeasurementModels that cannot generate measurements at the same time.
state_interp(query_stamps, state_list[, method])	Performs "linear" (geodesic) interpolation between State objects.
van_loans(A_c, L_c, Q_c, dt)	Van Loan's method for computing the discrete-time A and Q matrices.

Classes

GaussianResult(estimate, state_true)	A data container that simultaneously computes various interesting metrics about a Gaussian filter's state estimate, given the ground-truth value of the state.
GaussianResultList(result_list)	A data container that accepts a list of GaussianResult objects and stacks the attributes in numpy arrays.
MixtureResult(imm_estimate, state_true)	param estimate: Estimated state and corresponding covariance.
MixtureResultList(result_list)	param result_list: A list of GaussianResult, intended such that each element corresponds
MonteCarloResult(trial_results)	A data container which computes various interesting metrics associated with Monte Carlo experiments, such as the average estimation error squared (EES) and the average normalized EES.

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