A Specialized Database for Autonomous Vehicles Based on the KITTI Vision Benchmark

Abstract

Autonomous driving systems have emerged with the promise of preventing accidents. The first critical aspect of these systems is perception, where the regular practice is the use of top-view point clouds as the input; however, the existing databases in this area only present scenes with 3D point clouds and their respective labels. This generates an opportunity, and the objective of this work is to present a database with scenes directly in the top-view and their labels in the respective plane, as well as adding a segmentation map for each scene as a label for segmentation work. The method used during the creation of the proposed database is presented; this covers how to transform 3D to 2D top-view image point clouds, how the detection labels in the plane are generated, and how to implement a neural network for the generated segmentation maps of each scene. Using this method, a database was developed with 7481 scenes, each with its corresponding top-view image, label file, and segmentation map, where the road segmentation metrics are as follows: F1, 95.77; AP, 92.54; ACC, 97.53; PRE, 94.34; and REC, 97.25. This article presents the development of a database for segmentation and detection assignments, highlighting its particular use for environmental perception works.

A review of visual SLAM for robotics: evolution, properties, and future applications

“…As a valuable asset, researchers routinely rely on the KITTI dataset to evaluate the effectiveness of V-SLAM techniques in real-time tracking scenarios. In addition, it serves as an essential tool for researchers and developers engaged in the domains of self-driving cars and mobile robotics ( Geiger et al, 2012 ; Ortega-Gomez et al, 2023 ). Furthermore, its adaptability facilitates the evaluation of sensor configurations, thereby contributing to the refinement and assessment of algorithms crucial to these fields Geiger et al (2013) .…”

The Distributed HTAP Architecture for Real-Time Analysis and Updating of Point Cloud Data

“…However, this system did not load point cloud data into a database, so analysis and operations related to point clouds could not be conducted in real-time. Furthermore, Juan et al have developed a labeled database for autonomous driving systems using point cloud data captured through LiDAR [15]. He processed overlapping areas of the images, segmented each map, and built a database to enable neural networks to detect objects.…”