In this paper, we present an end-to-end framework for precise large-scale mapping with
applications in autonomous driving. In special, the problem of mapping complex …

The ability to drive autonomously in heterogeneous environments without GPS, pattern
identification (eg road following), or artificial landmarks is key to field robotics. To address …

Accurate localization and mapping in a large-scale environment is an essential system of an
autonomous vehicle. The difficulty of the previous LiDAR or LiDAR-inertial simultaneous …

In this paper we propose a fast 3D pose based SLAM system that estimates a vehicle's
trajectory by registering sets of planar surface segments, extracted from 36 0∘ field of view …

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 …

For autonomous driving, it is important to navigate in an unknown environment. In this paper,
we propose a fully automated 2D simultaneous localization and mapping (SLAM) system …

The Simultaneous Localization And Mapping (SLAM) problem has been well studied in the
robotics community, especially using mono, stereo cameras or depth sensors. 3D depth …

The terrestrial acquisition of 3D point clouds by laser range finders has recently moved to
mobile platforms. Measuring the environment while simultaneously moving the vehicle …

The high diversity of urban environments, at both the inter and intra levels, poses challenges
for robotics research. Such challenges include discrepancies in urban features between …

Accurately localizing in and mapping an environment are essential building blocks of most
autonomous systems. In this paper, we present a novel approach for LiDAR odometry and …