可以仿照CorrectErrorEstimationPoseVel
中的给CorrectErrorEstimationPose
加上运动模型约束
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Test1
error_state_kalman_filter: earth: # gravity can be calculated from https://www.sensorsone.com/local-gravity-calculator/ using latitude and height: gravity_magnitude: 9.80943 # rotation speed, rad/s: rotation_speed: 7.292115e-5 # latitude: latitude: 48.9827703173 covariance: prior: pos: 1.0e-6 vel: 1.0e-6 ori: 1.0e-6 epsilon: 1.0e-6 delta: 1.0e-6 process: gyro: 1.0e-4 accel: 2.5e-3 measurement: pose: pos: 1.0e-4 ori: 1.0e-4 pos: 1.0e-4 vel: 2.5e-3 motion_constraint: activated: true w_b_thresh: 0.13
APE laser fused max 2.706153 2.783004 mean 1.750127 1.751943 median 1.728464 1.731996 min 1.000020 0.891633 rmse 1.759152 1.762624 sse 13607.027875 13660.785561 std 0.177962 0.193746 -
Test2
error_state_kalman_filter: earth: # gravity can be calculated from https://www.sensorsone.com/local-gravity-calculator/ using latitude and height: gravity_magnitude: 9.80943 # rotation speed, rad/s: rotation_speed: 7.292115e-5 # latitude: latitude: 48.9827703173 covariance: prior: pos: 1.0e-6 vel: 1.0e-6 ori: 1.0e-6 epsilon: 1.0e-6 delta: 1.0e-6 process: gyro: 1.0e-4 accel: 2.5e-3 measurement: pose: pos: 1.0e-3 # x10 ori: 1.0e-3 # x10 pos: 1.0e-3 # x10 vel: 2.5e-3 motion_constraint: activated: true w_b_thresh: 0.13
APE laser fused max 1.847445 1.890410 mean 0.902068 0.917762 median 0.873098 0.892060 min 0.367366 0.358313 rmse 0.919268 0.937960 sse 3708.941335 3861.305624 std 0.176994 0.193600
这个好像第八章作业框架代码已经实现了啊
配置文件里选择position_velocity
融合策略