文件列表:

LICENSE (553, 2016-09-01)
MATLAB (0, 2016-09-01)
MATLAB\GrovesCode (0, 2016-09-01)
MATLAB\GrovesCode\CTM_to_Euler.m (800, 2016-09-01)
MATLAB\GrovesCode\ECEF_to_NED.m (2445, 2016-09-01)
MATLAB\GrovesCode\Euler_to_CTM.m (1275, 2016-09-01)
MATLAB\GrovesCode\Gravity_ECEF.m (1400, 2016-09-01)
MATLAB\GrovesCode\Initialize_LC_P_matrix.m (1245, 2016-09-01)
MATLAB\GrovesCode\Kinematics_ECEF.m (3647, 2016-09-01)
MATLAB\GrovesCode\License.txt (1516, 2016-09-01)
MATLAB\GrovesCode\NED_to_ECEF.m (1959, 2016-09-01)
MATLAB\GrovesCode\Nav_equations_ECEF.m (3475, 2016-09-01)
MATLAB\GrovesCode\Skew_symmetric.m (507, 2016-09-01)
MATLAB\GrovesCode\pv_ECEF_to_NED.m (2210, 2016-09-01)
MATLAB\LC_KF_Epoch.m (7492, 2016-09-01)
MATLAB\Loosely_coupled_INS_GNSS.m (9657, 2016-09-01)
MATLAB\Tuning (0, 2016-09-01)
MATLAB\Tuning\tune_R_minmax.m (9986, 2016-09-01)
MATLAB\Tuning\tune_bias_psd.m (2440, 2016-09-01)
MATLAB\Tuning\tune_bias_unc.m (1824, 2016-09-01)
MATLAB\Tuning\tune_epoch.m (1623, 2016-09-01)
MATLAB\Tuning\tune_gps_correction.m (2102, 2016-09-01)
MATLAB\Tuning\tune_init_heading.m (2244, 2016-09-01)
MATLAB\Tuning\tune_lag.m (1565, 2016-09-01)
MATLAB\Tuning\tune_lever.m (1987, 2016-09-01)
MATLAB\Tuning\tune_n.m (1869, 2016-09-01)
MATLAB\Tuning\tune_noise_psd.m (2308, 2016-09-01)
MATLAB\Tuning\tune_pose_sd.m (1961, 2016-09-01)
MATLAB\Tuning\tune_pose_unc.m (2442, 2016-09-01)
MATLAB\Utilities (0, 2016-09-01)
MATLAB\Utilities\CalibAccel.m (8553, 2016-09-01)
MATLAB\Utilities\deg2utm.m (3937, 2016-09-01)
MATLAB\Utilities\figurec.m (1425, 2016-09-01)
MATLAB\Utilities\inversePD.m (300, 2016-09-01)
MATLAB\Utilities\nearestSPD.m (1884, 2016-09-01)
MATLAB\adapt_noise_covariance.m (598, 2016-09-01)
MATLAB\generate_error_metrics.m (1417, 2016-09-01)
... ...

# kalman-localization Implementation of localization using sensor fusion of GPS/INS/compass through an error-state Kalman filter. The MATLAB code borrows heavily from Paul D. Groves' book, *Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems*, his MATLAB code is marked as his, and is held under the BSD license. This code is very much a work-in-progress as I transition from a MATLAB implementation to C++. Until the code is in a stable state, I will make changes based on my own needs, with complete disregard for backwards compatibility. If you liked it, then you shoulda used a fork on github. Following the initial development, I may make it more portable. PLEASE NOTE: I cannot provide my testing data as it was collected using a vehicle owned by General Motors. Please, refer to the comments in the initialization scripts for a description of the data format and you should be able to adapt your own data. Alternatively, the book I used comes with a CD-ROM that has his code (fully working filter) along with a system for generating test data.

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