I have a Velodyne VLP-16 "Puck" lidar that I am trying to use with Google Cartographer in ROS. I've set "npackets" in the velodyne driver to 1, to get the message frequency we see in Google's sample bag files, but the messages seem to be published sporadically (see the attached RQT image).
What could be the limiting factor here?
I've tried this with direct ethernet connections between my VLP-16 and the computer, and with computers running Ubuntu with an Intel Core I5-7600 processor and a Core I5-8365U processor.
Are these processors just not fast enough, or is there some other bottleneck causing these pauses in messages?
As a result, the Cartographer validator complains that one message begins before the previous message ends:
cartographer_rosbag_validate -bag_filename 2021-08-10-17-10-09.bag
W0810 17:19:57.937173 4886 rosbag_validate_main.cc:166] Sensor with frame_id "velodyne" measurements overlap in time. Previous range message, ending at time stamp 637642374093122761, must finish before current range message, which ranges from 637642374093120291 to 637642374093133354
W0810 17:19:57.940515 4886 rosbag_validate_main.cc:166] Sensor with frame_id "velodyne" measurements overlap in time. Previous range message, ending at time stamp 637642374093783810, must finish before current range message, which ranges from 637642374093783614 to 637642374093796654
W0810 17:19:57.940832 4886 rosbag_validate_main.cc:166] Sensor with frame_id "velodyne" measurements overlap in time. Previous range message, ending at time stamp 637642374093929761, must finish before current range message, which ranges from 637642374093929736 to 637642374093942776
W0810 17:19:59.675483 4886 rosbag_validate_main.cc:203] Sensor with frame_id "velodyne" range measurements have longest overlap of 0.0012975 s
I0810 17:19:59.675602 4886 rosbag_validate_main.cc:399] Time delta histogram for consecutive messages on topic "points2" (frame_id: "velodyne"):
Count: 8126 Min: 8.821e-06 Max: 0.0126982 Mean: 0.00132742
[0.000009, 0.001278) # Count: 295 (3.63032%) Total: 295
(3.63032%)
[0.001278, 0.002547) ################### Count: 7828 (96.3328%) Total: 8123 (99.9631%)
[0.002547, 0.003816) Count: 2 (0.0246124%) Total: 8125 (99.9877%)
[0.003816, 0.005085) Count: 0 (0%) Total: 8125 (99.9877%)
[0.005085, 0.006353) Count: 0 (0%) Total: 8125 (99.9877%)
[0.006353, 0.007622) Count: 0 (0%) Total: 8125 (99.9877%)
[0.007622, 0.008891) Count: 0 (0%) Total: 8125 (99.9877%)
[0.008891, 0.010160) Count: 0 (0%) Total: 8125 (99.9877%)
[0.010160, 0.011429) Count: 0 (0%) Total: 8125 (99.9877%)
[0.011429, 0.012698] Count: 1 (0.0123062%) Total: 8126 (100%)