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Comments (3)

clegenti avatar clegenti commented on August 15, 2024

Hi,
Thanks for the interest in our work.

In my C++ code I used the snark::velodyne::stream object from the snark library that I linked in my cmake. Then I pass the data with pipe like cat lidar.bin | in2laama_exe

By using snark to collect lidar data, you get a timestamp per point which allows for accurate motion distortion correction. The data is a "stream" of points. To have scans, you can segment the full set of points based on the timestamps or the azimuth of the points.

If you don't feel like dealing with snark in your code, you can probably convert the binary files to csv as shown there or velodyne-to-csv.
(it is probably possible to write an independent parser to transform the binary format into csv, but would need to get the underlying structure, not sure what it is. I can look which command we used in our sensor suite if that can help)

from in2laama_datasets.

icameling avatar icameling commented on August 15, 2024

Hello,

Thank you for your quick reply and sorry for my late response.

After installing the latest snark library, I used the following command in the folder /in2laama_datasets/staircase

cat lidar.bin | velodyne-to-csv --model vlp16  > lidar.csv

and got lidar.csv with a size of 2.8GB. Note that the size of lidar.bin is 83.6 MB. The following is some data from the beginning and end of lidar.csv. Each row has thirteen items and the first two items seems to be timestamps and rings of points. Do you have any idea what the rest of the items means?

20190402T074603.248383,1,5355,0,0,0,-1.47004921858,1.37326846792,0.0351142417518,-3.89296598716,2.012,1,1
20190402T074603.248385,2,5865,0,0,0,-1.71026456299,1.59769834041,-0.540332432534,-3.89297507774,2.402,1,1
20190402T074603.248387,3,7395,0,0,0,-1.4405032011,1.3457166597,0.103311177624,-3.89298416833,1.974,1,1
20190402T074603.248390,4,5865,0,0,0,-1.66702335766,1.55735994493,-0.443440105255,-3.89299325891,2.324,1,1
20190402T074603.248392,5,7650,0,0,0,-1.40930461962,1.31661896122,0.168733517959,-3.8930023495,1.936,1,1
20190402T074603.248394,6,5610,0,0,0,-1.61955010209,1.51306481346,-0.35103893955,-3.89301144009,2.244,1,1
20190402T074603.248397,7,9180,0,0,0,-1.38381170544,1.29284975706,0.232526707217,-3.89302053067,1.908,1,1
20190402T074603.248399,8,4590,0,0,0,-1.58832384499,1.48394576378,-0.266893862057,-3.89302962126,2.19,1,1
20190402T074603.248401,9,8415,0,0,0,-1.34959069796,1.26092415073,0.292532449625,-3.89303871184,1.87,1,1
20190402T074603.248404,10,7140,0,0,0,-1.56647456281,1.46358566752,-0.187559158393,-3.89304780243,2.152,1,1
20190402T074603.248406,11,9435,0,0,0,-1.31689885055,1.23042496347,0.350325315511,-3.89305689301,1.836,1,1
20190402T074603.248408,12,4845,0,0,0,-1.52795498646,1.42764815839,-0.109591492373,-3.8930659836,2.094,1,1
20190402T074603.248410,13,9435,0,0,0,-1.2929018731,1.20804777046,0.408511114688,-3.89307507418,1.816,1,1
20190402T074603.248413,14,5865,0,0,0,-1.4874203611,1.38982519341,-0.0355330995063,-3.89308416477,2.036,1,1
20190402T074603.248415,15,9435,0,0,0,-1.26755998345,1.18441225415,0.464839005004,-3.89309325535,1.796,1,1
20190402T074603.248433,0,5100,0,0,0,-1.74417151429,1.6335648689,-0.640318317584,-3.89425672402,2.474,1,1
20190402T074603.248436,1,4590,0,0,0,-1.45366790588,1.361508355,0.0347651936231,-3.8942658146,1.992,1,1
20190402T074603.248438,2,5610,0,0,0,-1.6868518356,1.57993769037,-0.533583900904,-3.89427490519,2.372,1,1
20190402T074603.248440,3,7650,0,0,0,-1.43875278525,1.34758792793,0.103311177624,-3.89428399577,1.974,1,1
20190402T074603.248443,4,5610,0,0,0,-1.65926616144,1.55415705326,-0.441913633292,-3.89429308636,2.316,1,1
20190402T074603.248445,5,7650,0,0,0,-1.4075920521,1.31844970127,0.168733517959,-3.89430217694,1.936,1,1
20190402T074603.248447,6,6375,0,0,0,-1.61469862093,1.51246783492,-0.35041320169,-3.89431126753,2.24,1,1
20190402T074603.248450,7,9180,0,0,0,-1.36329599795,1.27700543537,0.229358104288,-3.89432035811,1.882,1,1
20190402T074603.248452,8,4590,0,0,0,-1.58349610771,1.48329488473,-0.266406384684,-3.8943294487,2.186,1,1
20190402T074603.248454,9,8670,0,0,0,-1.34506725777,1.25997640601,0.291906711765,-3.89433853928,1.866,1,1
20190402T074603.248457,10,7140,0,0,0,-1.5500302101,1.45199956809,-0.185816043538,-3.89434762987,2.132,1,1
20190402T074603.248459,11,10710,0,0,0,-1.30670167682,1.22408249054,0.348035607567,-3.89435672045,1.824,1,1
20190402T074603.248461,12,5355,0,0,0,-1.51443727024,1.41870937705,-0.108754117073,-3.89436581104,2.078,1,1
20190402T074603.248463,13,9690,0,0,0,-1.29417486805,1.21239189637,0.409410918906,-3.89437490162,1.82,1,1
20190402T074603.248466,14,4590,0,0,0,-1.4826938834,1.38902311111,-0.0354632898806,-3.89438399221,2.032,1,1
20190402T074603.248468,15,9690,0,0,0,-1.26742920267,1.18737964061,0.465356643094,-3.8943930828,1.798,1,1
20190402T074603.248486,0,5355,0,0,0,-1.73345985659,1.64211936719,-0.639800679493,-3.89993821358,2.472,1,1
20190402T074603.248489,1,4845,0,0,0,-1.44300565843,1.36699484274,0.0346953839973,-3.89994730417,1.988,1,1
20190402T074603.248491,2,5610,0,0,0,-1.67218941182,1.58413510701,-0.531784292469,-3.89995639475,2.364,1,1
20190402T074603.248493,3,7905,0,0,0,-1.4151241536,1.34063082462,0.102159786586,-3.89996548534,1.952,1,1

......

20190402T074604.599272,1,7650,0,0,0,0.888643238192,-0.658161541003,0.0193023615196,-0.63748143038,1.106,1,15
20190402T074604.599274,2,9180,0,0,0,0.873823393523,-0.647197725426,-0.251045376648,-0.637490520965,1.116,1,15
20190402T074604.599276,3,7905,0,0,0,0.897178433763,-0.664508277423,0.0585115990796,-0.63749961155,1.118,1,15
20190402T074604.599278,4,9180,0,0,0,0.881897418928,-0.65320258619,-0.213324456831,-0.637508702136,1.118,1,15
20190402T074604.599281,5,7650,0,0,0,0.888574758322,-0.658160858086,0.0967428744499,-0.637517792721,1.11,1,15
20190402T074604.599283,6,7650,0,0,0,0.869869942014,-0.644318582783,-0.171452173684,-0.637526883306,1.096,1,15
20190402T074604.599285,7,9180,0,0,0,0.885308445648,-0.655766450984,0.13527497118,-0.637535973892,1.11,1,15
20190402T074604.599288,8,5865,0,0,0,0.890087903145,-0.65931922595,-0.13600618724,-0.637545064477,1.116,1,15
20190402T074604.599290,9,8160,0,0,0,0.900011485009,-0.666682640831,0.177396683356,-0.637554155062,1.134,1,15
20190402T074604.599292,10,9435,0,0,0,0.893347787159,-0.661759090301,-0.0972658089064,-0.637563245647,1.116,1,15
20190402T074604.599295,11,9690,0,0,0,0.875545612537,-0.648584222729,0.211797984868,-0.637572336233,1.11,1,15
20190402T074604.599297,12,5355,0,0,0,0.881075332821,-0.652692918198,-0.0574648799548,-0.637581426818,1.098,1,15
20190402T074604.599299,13,9435,0,0,0,0.900378559533,-0.667005256837,0.258693712495,-0.637590517403,1.15,1,15
20190402T074604.599302,14,7650,0,0,0,0.883745024623,-0.654695487843,-0.019197647081,-0.637599607989,1.1,1,15
20190402T074604.599304,15,8925,0,0,0,0.884802113773,-0.655491057789,0.295053711417,-0.637608698574,1.14,1,15
20190402T074604.599377,1,7650,0,0,0,0.891406799823,-0.66784124357,0.0194419807711,-0.64298838702,1.114,1,15
20190402T074604.599379,2,9180,0,0,0,0.86400774863,-0.647326159934,-0.249245768213,-0.642997477605,1.108,1,15
20190402T074604.599381,3,7905,0,0,0,0.891907030154,-0.668241333965,0.0584069271671,-0.643006568191,1.116,1,15
20190402T074604.599384,4,7905,0,0,0,0.883000431798,-0.661580797914,-0.214469310803,-0.643015658776,1.124,1,15
20190402T074604.599386,5,8160,0,0,0,0.886531320447,-0.664238870391,0.0969171859354,-0.643024749361,1.112,1,15
20190402T074604.599388,6,8160,0,0,0,0.883697940305,-0.662128486654,-0.174893731915,-0.643033839947,1.118,1,15
20190402T074604.599391,7,8670,0,0,0,0.888038239905,-0.665393148561,0.136249925927,-0.643042930532,1.118,1,15
20190402T074604.599393,8,6120,0,0,0,0.881677756772,-0.66063985301,-0.13527497118,-0.643052021117,1.11,1,15
20190402T074604.599395,9,8160,0,0,0,0.899488109449,-0.673997892062,0.178022421216,-0.643061111703,1.138,1,15
20190402T074604.599398,10,9435,0,0,0,0.892878833886,-0.669058151947,-0.0976144318774,-0.643070202288,1.12,1,15
20190402T074604.599400,11,9690,0,0,0,0.887671631612,-0.665168855539,0.215614164775,-0.643079292873,1.13,1,15
20190402T074604.599402,12,5355,0,0,0,0.877467639425,-0.657535040384,-0.0574648799548,-0.643088383459,1.098,1,15
20190402T074604.599404,13,8670,0,0,0,0.888894436787,-0.666110389901,0.256444201952,-0.643097474044,1.14,1,15
20190402T074604.599407,14,7650,0,0,0,0.886527181011,-0.664349025331,-0.0193372663325,-0.643106564629,1.108,1,15
20190402T074604.599409,15,8670,0,0,0,0.890454522656,-0.667304751829,0.298159539958,-0.643115655214,1.152,1,15
20190402T074604.599427,0,8925,0,0,0,0.881954636735,-0.662538750634,-0.295571349507,-0.644279123877,1.142,1,15
20190402T074604.599430,1,6120,0,0,0,0.88893915722,-0.667798278049,0.0194070759583,-0.644288214462,1.112,1,15
20190402T074604.599432,2,9180,0,0,0,0.869397849315,-0.653130613637,-0.251045376648,-0.644297305048,1.116,1,15
20190402T074604.599434,3,7905,0,0,0,0.891037678514,-0.669400094359,0.0584069271671,-0.644306395633,1.116,1,15
20190402T074604.599437,4,9945,0,0,0,0.879000457877,-0.660369524309,-0.213706074822,-0.644315486218,1.12,1,15
20190402T074604.599439,5,8160,0,0,0,0.893631808734,-0.671374375509,0.0977887433629,-0.644324576804,1.122,1,15
20190402T074604.599441,6,6885,0,0,0,0.879677913189,-0.660903499231,-0.174267994055,-0.644333667389,1.114,1,15
20190402T074604.599444,7,7905,0,0,0,0.883998452022,-0.66416209948,0.135762448553,-0.644342757974,1.114,1,15

from in2laama_datasets.

icameling avatar icameling commented on August 15, 2024

With the help of the following command

view-points --fields=t,id,,,,,x,y,z,,,, lidar.csv "lidar.bin;binary=3d,3ub"

I got the visualization of lidar.bin, and I think the meaning of 13 items in each row are

t,id,,,,,x,y,z,,,,

The remaining work is to split lidar.csv into individual scan.
image

from in2laama_datasets.

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