LiDAR Point Cloud Generation for SLAM Algorithm Evaluation

Abstract

With the emerging interest in the autonomous driving level at 4 and 5 comes a necessity to provide accurate and versatile frameworks to evaluate the algorithms used in autonomous vehicles. There is a clear gap in the field of autonomous driving simulators. It covers testing and parameter tuning of a key component of autonomous driving systems, SLAM, frameworks targeting off-road and safety-critical environments. It also includes taking into consideration the non-idealistic nature of the real-life sensors, associated phenomena and measurement errors. We created a LiDAR simulator that delivers accurate 3D point clouds in real time. The point clouds are generated based on the sensor placement and the LiDAR type that can be set using configurable parameters. We evaluate our solution based on comparison of the results using an actual device, Velodyne VLP-16, on real-life tracks and the corresponding simulations. We measure the error values obtained using Google Cartographer SLAM algorithm and the distance between the simulated and real point clouds to verify their accuracy. The results show that our simulation (which incorporates measurement errors and the rolling shutter effect) produces data that can successfully imitate the real-life point clouds. Due to dedicated mechanisms, it is compatible with the Robotic Operating System (ROS) and can be used interchangeably with data from actual sensors, which enables easy testing, SLAM algorithm parameter tuning and deployment.

Three-Dimensional Linear Restoration of a Tunnel Based on Measured Track and Uncontrolled Mobile Laser Scanning

“…Additionally, we will explore the application of this method in other scenarios. On the other hand, we will also try to improve the SLAM algorithm in order to improve the application effect of the tunnel in this repetitively structured environment [ 36 , 37 ].…”

Real-Time 3D Mapping in Isolated Industrial Terrain with Use of Mobile Robotic Vehicle

“…The last one, also called an occupancy map, is the most common way for robots to describe the model of the environment. It divides the workspace into a series of grids, where each cell in the grid corresponds to binary random variables that describe the occupancy probability [8,20,[40][41][42]. Hence, the map m is given by a product occupancy probability of each cell mi, which is associated with a certain point in three-dimensional Cartesian space [23,34]:…”

“…There are many different SLAM techniques based on occupancy grids for map building in 2D or 3D space [10,22]. One of possible representations of the 3D map as an occupancy grid is octrees, which instead of the 2D quadtrees representation presented directly in references [8,42], uses an octree hierarchical data structure, where each volume-named node has eight child connections with inner nodes [25]. Currently, in the scientific literature, a rich number of occupancy grids-based SLAM techniques can be found [18,20,26,43].…”

“…The map-building approach applied in this paper has its basis in a robot's pose estimation, which is presented in references [42,49], called the AMCL-EKF, which connects two separate paradigms-Kalman and particle filters. The pose is estimated by the extended Kalman filter by using measurements data from LIDAR and IMU sensors and adaptive Monte-Carlo localization (AMCL).…”

Finding the best hardware configuration for 2D SLAM in indoor environments via simulation based on Google Cartographer

“…In this work, we present the cost-efficient evaluation methodology that can be used to test and compare different SLAM algorithms based on data from LiDARs, IMU and odometry. For that purpose, we utilize a simulated environment introduced in 12 . The framework was comprehensively evaluated in that study.…”