ASU Pedestrian LiDAR Scenes Dataset

ASU Pedestrian LiDAR Scenes (APLS) Dataset

Introduction

Manifest files for APLS-1, APLS-2, APLS-3, and stats_file.csv

Welcome to the ASU Pedestrian LiDAR Scenes (APLS) Dataset. Currently, the dataset is being compressed for upload. A sample can be downloaded here (APLS-sample)

We have collected the APLS dataset to facilitate efforts in point cloud representation learning using a semi-supervised "next frame given previous frames" prediction task.

Description

APLS contains 30 hours of Velodyne HDL-32e point cloud data. It was captured in 20 collection runs in 10 different pedestrian walking areas around the campus of Arizona State University in 2019. The dataset contains ~1 million LiDAR frames.

Table 1: Collection run details

Repo #	Run Name	Location	Time of Day	Weather	Traffic Level	N Frames	Duration
1	Bookstore-1	Bookstore	Afternoon	Sunny	High	72939	2 hr, 1 min
1	Bookstore-2	Bookstore	Afternoon	Sunny	High	39130	1 hr, 5 min
1	Church-1	Church	Morning	Sunny	Moderate	58009	1 hr, 36 min
1	Church-2	Church	Afternoon	Sunny	Moderate	58793	1 hr, 38 min
1	Courtyard	Courtyard	Afternoon	Sunny	Sparse	92785	2 hr, 34 min
1	Thoroughfare	Thoroughfare	Mid-day	Sunny	Moderate	53919	1 hr, 29 min
1	Pavilion-1	Pavilion	Morning	Sunny	Moderate	55541	1 hr, 33 min
2	Pavilion-2	Pavilion	Afternoon	Sunny	Moderate	54148	1 hr, 30 min
2	Gymnasium-1	Gymnasium	Sunset & Evening	Night	High	55613	1 hr, 32 min
2	Gymnasium-2	Gymnasium	Sunset & Evening	Night	High	6727	0 hr, 11 min
2	Gymnasium-3	Gymnasium	Sunset & Evening	Night	High	45305	1 hr, 15 min
2	Intersection-1	Intersection	Morning	Sunny	Moderate	56159	1 hr, 33 min
2	Intersection-2	Intersection	Afternoon	Overcast	Moderate	53684	1 hr, 29 min
3	Boulevard	Boulevard	Morning	Sunny	Low	55284	1 hr, 32 min
3	Fountain-1	Fountain	Morning	Sunny	High	52111	1 hr, 27 min
3	Fountain-2	Fountain	Afternoon	Overcast	Moderate	24714	0 hr, 41 min
3	Bench-1	Bench	Morning	Sunny	Moderate	54117	1 hr, 30 min
3	Bench-2	Bench	Afternoon	Sunny	Moderate	55512	1 hr 32 min
3	Bench-3	Bench	Evening	Night	Moderate	52110	1 hr, 27 min
3	Bench-4	Bench	Afternoon & Evening	Sunset/Night	Moderate	107714	3 hr, 0 min

Figure 1: Campus collection locations

Figure 2: 3D sample of a frame

Figure 3: "Depth camera" sample of a frame

Technical Details

For space saving purposes, the data is stored in "Distance-only Velodyne" (DOVe) format. The files contain only the parts of the captured LiDAR bytestream that contain actual distance data, and all other data is discarded. Each frame is stored as a single file, rather than stored as a chunk of bytes in a package capture file containing all frames from a given run, as the Velodyne PCAP output data is initially stored. Thus, in addition to saving space, it enables in-place arbitrary reading of any frame in the dataset in O(1) without having to advance past all prior frames in time for a run.

The Velodyne Dataset Utils repository contains the file "dove_dataset.py," which defines a Pytorch dataloader (DOVeDataset class) for a directory of DOVe format binary frames. The repo contains usage examples on loading a DOVe dataset such as APLS. Finally, the repo contains "parser.py," which can be used to convert any Velodyne HDLE-32e PCAP file into a DOVe dataset. We will extend the functionality of parser.py to support other LiDAR devices if there is interest.

Usage

A sample may be downloaded from APLS-sample and unzipped using the following:

$	cd APLS-sample/
$	unzip Gym-2.zip
$	cd ..

The dataset can be initialized as Pytorch datasets using the following code, for example for the directory containing "APLS-sample":

1	import dove_dataset
2	from torch.utils.data import DataLoader
3
4	d_train, d_val, d_test = dove_dataset.CreateDOVeDatasets('./APLS-sample/', frames_per_clip=10, raw=False, granularity=3)
5	loader_train = DataLoader(d_train, batch_size=32, shuffle=True, num_workers=3)
6	for minibatch in d_train:
7	...