@robot-pesg
Thank for your hard work !
The dataset is so useful for me.
But i wonder that can you provide groundtruth map for me to compare with SLAM algorithm map.
Thank you !

Where is the download link for the Ground Truth Map? Thanks!!

启动后要等较久rviz才出现点云，而且一出现就是飞的。

包名为1018_00_img10hz600p.bag

When I run that dataset with VINS-MOMO, the terminal keeps having the error message, "IMU excitation not enouth! Not enough features or parallax; Move device around",can you help me and give me some advice?
Here are my parameter settings for the yaml file。

%YAML:1.0

#common parameters
#support: 1 imu 1 cam; 1 imu 2 cam: 2 cam;

imu: 1

num_of_cam: 1

imu_topic: "/imu/data"
image_topic: "/dalsa_gray/left/image_raw"
output_path: "/home/zhang/output/"

distortion_parameters:
k1: -0.061606731291039
k2: 0.100129900708930
p1: 0.0
p2: 0.0
projection_parameters:
fx: 643.5951111267952
fy: 642.6656126857330
cx: 475.2215406900663
cy: 307.4120184196884
image_width: 960
image_height: 600

Extrinsic parameter between IMU and Camera.

estimate_extrinsic: 0 # 0 Have an accurate extrinsic parameters. We will trust the following imu^R_cam, imu^T_cam, don't change it.
# 1 Have an initial guess about extrinsic parameters. We will optimize around your initial guess.

extrinsicRotation: !!opencv-matrix
rows: 3
cols: 3
dt: d
data: [-0.00375313,0.01609339,0.99986345,
-0.99997267,-0.00642959,-0.00365005,
0.00636997,-0.99984982,0.01611708]
#Translation from camera frame to imu frame, imu^T_cam
extrinsicTranslation: !!opencv-matrix
rows: 3
cols: 1
dt: d
data: [0.17517298,0.13259005, 0.0670137]

#Multiple thread support
multiple_thread: 4 # origin 1

#feature traker paprameters
max_cnt: 150 # max feature number in feature tracking
min_dist: 30 # min distance between two features
freq: 0 # frequence (Hz) of publish tracking result. At least 10Hz for good estimation. If set 0, the frequence will be same as raw image
F_threshold: 1.0 # ransac threshold (pixel)
show_track: 1 # publish tracking image as topic
flow_back: 1 # perform forward and backward optical flow to improve feature tracking accuracy

#optimization parameters
max_solver_time: 0.04 # max solver itration time (ms), to guarantee real time origin 0.04
max_num_iterations: 8 # max solver itrations, to guarantee real time origin 8
keyframe_parallax: 10.0 # keyframe selection threshold (pixel)

#imu parameters The more accurate parameters you provide, the better performance
acc_n: 0.001 # accelerometer measurement noise standard deviation.
gyr_n: 0.0002 # gyroscope measurement noise standard deviation.
acc_w: 0.00002 # accelerometer bias random work noise standard deviation.
gyr_w: 0.000005 # gyroscope bias random work noise standard deviation.
g_norm: 9.81007 # gravity magnitude

#unsynchronization parameters
estimate_td: 0 # online estimate time offset between camera and imu
td: 0.0 # initial value of time offset. unit: s. readed image clock + td = real image clock (IMU clock)

#loop closure parameters
loop_closure:1
load_previous_pose_graph: 0 # load and reuse previous pose graph; load from 'pose_graph_save_path'
pose_graph_save_path: "/home/zhang/output/pose_gragh/" # save and load path
save_image: 1 # save image in pose graph for visualization prupose; you can close this function by setting 0

Is wheel encoder data available? That is mentioned in the sensor setup section of readme.