Assessing Temporal Behavior in Lidar Point Clouds of Urban Environments

Abstract

ABSTRACT:Self-driving cars and robots that run autonomously over long periods of time need high-precision and up-to-date models of the changing environment. The main challenge for creating long term maps of dynamic environments is to identify changes and adapt the map continuously. Changes can occur abruptly, gradually, or even periodically. In this work, we investigate how dense mapping data of several epochs can be used to identify the temporal behavior of the environment. This approach anticipates possible future scenarios where a large fleet of vehicles is equipped with sensors which continuously capture the environment. This data is then being sent to a cloud based infrastructure, which aligns all datasets geometrically and subsequently runs scene analysis on it, among these being the analysis for temporal changes of the environment. Our experiments are based on a LiDAR mobile mapping dataset which consists of 150 scan strips (a total of about 1 billion points), which were obtained in multiple epochs. Parts of the scene are covered by up to 28 scan strips. The time difference between the first and last epoch is about one year. In order to process the data, the scan strips are aligned using an overall bundle adjustment, which estimates the surface (about one billion surface element unknowns) as well as 270,000 unknowns for the adjustment of the exterior orientation parameters. After this, the surface misalignment is usually below one centimeter. In the next step, we perform a segmentation of the point clouds using a region growing algorithm. The segmented objects and the aligned data are then used to compute an occupancy grid which is filled by tracing each individual LiDAR ray from the scan head to every point of a segment. As a result, we can assess the behavior of each segment in the scene and remove voxels from temporal objects from the global occupancy grid.

Integrity and Collaboration in Dynamic Sensor Networks

“…Some preliminary results have been presented in [ 68 ], based on LiDAR data from earlier measurement campaigns in Hannover-Badenstedt. There, we analyzed the temporal behavior of typical objects in an urban scene and distinguished between static and dynamic objects by a simple threshold.…”

Building Change Detection of Airborne Laser Scanning and Dense Image Matching Point Clouds using Height and Class Information

“…On a local level using point clouds from mobile mapping, 3D voxel grids track the occupancy of a voxel. From these occupancies, short and long term changes can be derived (Schachtschneider et al, 2017).…”

A Fast Voxel-Based Indicator for Change Detection Using Low Resolution Octrees

“…The actual change detection is then executed based on these units. Schachtschneider et al (2017) assess the temporal behavior of clusters extracted from point clouds of urban environments using an occupancy grid. Aijazi et al (2013) classify clusters into known permanent and temporary classes.…”