16 accepted papers
While global point cloud registration systems have advanced significantly in all aspects, many studies have focused on specific components, such as feature extraction, graph-theoretic pruning, or pose solvers. In this paper, we take a holistic view on the registration problem and develop an open-sou
Robust and accurate localization and mapping of an environment using laser scanners, so-called LiDAR SLAM, is essential to many robotic applications. Early 3D LiDAR SLAM methods often exploited additional information from IMU or GNSS sensors to enhance localization accuracy and mitigate drift. Later
The ability to detect loop closures plays an essential role in any SLAM system. Loop closures allow correcting the drifting pose estimates from a sensor odometry pipeline. In this paper, we address the problem of effectively detecting loop closures in LiDAR SLAM systems in various environments with…
Odometry and mapping play a pivotal role in the navigation of autonomous vehicles. In this paper, we address the problem of pose estimation and map creation using only radar sensors. We focus on two odometry estimation approaches followed by a mapping step. The first one is a new point-to-point ICP…
Mobile robots that navigate in unknown environments need to be constantly aware of the dynamic objects in their surroundings for mapping, localization, and planning. It is key to reason about moving objects in the current observation and at the same time to also update the internal model of the stat
Robust and accurate pose estimation of a robotic platform, so-called sensor-based odometry, is an essential part of many robotic applications. While many sensor odometry systems made progress by adding more complexity to the ego-motion estimation process, we move in the opposite direction. By removi
Mapping an environment is essential for several robotic tasks, particularly for localization. In this letter, we address the problem of mapping the environment using LiDAR point clouds with the goal to obtain a map representation that is well suited for robot localization. To this end, we utilize a
Understanding the scene is key for autonomously navigating vehicles, and the ability to segment the surroundings online into moving and non-moving objects is a central ingredient of this task. Often, deep learning-based methods are used to perform moving object segmentation (MOS). The performance of
Monitoring plants and fruits is important in modern agriculture, with applications ranging from high-throughput phenotyping to autonomous harvesting. Obtaining highly accurate 3D measurements under real agricultural conditions is a challenging task. In this letter, we address the problem of estimati
Scene understanding is critical for autonomous navigation in dynamic environments. Perception tasks in this domain like segmentation and tracking are usually tackled individually. In this letter, we address the problem of 4D panoptic segmentation using LiDAR scans, which requires to assign to each 3
Reliable and accurate registration of point clouds is a challenging problem in robotics as well as in the domain of autonomous driving. In this article, we address the task of aligning point clouds with low overlap, containing moving objects, and without prior information about the initial guess. We
Mapping systems that turn sensor data into a model of the environment are standard components in mobile robotics. Outdoor robots are often equipped with 3D LiDAR sensors to obtain accurate range measurements at a high frame rate. The price for a robotic LiDAR sensor scales roughly linearly with the
A key challenge for autonomous vehicles is to navigate in unseen dynamic environments. Separating moving objects from static ones is essential for navigation, pose estimation, and understanding how other traffic participants are likely to move in the near future. In this work, we tackle the problem
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 mapping, focusing on improving the mapping quality and at the same time estimating the pose of the vehicle. Our approach per…
Robust and accurate, map-based localization is crucial for autonomous mobile systems. In this paper, we exploit range images generated from 3D LiDAR scans to address the problem of localizing mobile robots or autonomous cars in a map of a large-scale outdoor environment represented by a triangular m…
Perception in autonomous vehicles is often carried out through a suite of different sensing modalities. Given the massive amount of openly available labeled RGB data and the advent of high-quality deep learning algorithms for image-based recognition, high-level semantic perception tasks are pre-domi…