513 citations · 2.8k across the 104 of their papers we have counts for
14 papers · 1 filter
Self-supervised Point Cloud Prediction Using 3D Spatio-temporal Convolutional Networks
Benedikt Mersch, Xieyuanli Chen, Jens Behley +1
Exploiting past 3D LiDAR scans to predict future point clouds is a promising method for autonomous mobile systems to realize foresighted state estimation, collision avoidance, and…
Maneuver-based Trajectory Prediction for Self-driving Cars Using Spatio-temporal Convolutional Networks
Benedikt Mersch, Thomas Höllen, Kun Zhao +2
The ability to predict the future movements of other vehicles is a subconscious and effortless skill for humans and key to safe autonomous driving. Therefore, trajectory prediction…
Online Range Image-based Pole Extractor for Long-term LiDAR Localization in Urban Environments
Hao Dong, Xieyuanli Chen, Cyrill Stachniss
Reliable and accurate localization is crucial for mobile autonomous systems. Pole-like objects, such as traffic signs, poles, lamps, etc., are ideal landmarks for localization in u…
Video Contrastive Learning with Global Context
Haofei Kuang, Yi Zhu, Zhi Zhang +5
Contrastive learning has revolutionized self-supervised image representation learning field, and recently been adapted to video domain. One of the greatest advantages of contrastiv…
Adaptive Path Planning for UAV-based Multi-Resolution Semantic Segmentation
Felix Stache, Jonas Westheider, Federico Magistri +2
In this paper, we address the problem of adaptive path planning for accurate semantic segmentation of terrain using unmanned aerial vehicles (UAVs). The usage of UAVs for terrain m…
Inferring Objectives in Continuous Dynamic Games from Noise-Corrupted Partial State Observations
Lasse Peters, David Fridovich-Keil, Vicenç Rubies-Royo +2
Robots and autonomous systems must interact with one another and their environment to provide high-quality services to their users. Dynamic game theory provides an expressive theor…
Range Image-based LiDAR Localization for Autonomous Vehicles
Xieyuanli Chen, Ignacio Vizzo, Thomas Läbe +2
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…
Simple But Effective Redundant Odometry for Autonomous Vehicles
Andrzej Reinke, Xieyuanli Chen, Cyrill Stachniss
Robust and reliable ego-motion is a key component of most autonomous mobile systems. Many odometry estimation methods have been developed using different sensors such as cameras or…
Learning an Overlap-based Observation Model for 3D LiDAR Localization
Xieyuanli Chen, Thomas Läbe, Lorenzo Nardi +2
Localization is a crucial capability for mobile robots and autonomous cars. In this paper, we address learning an observation model for Monte-Carlo localization using 3D LiDAR data…
OverlapNet: Loop Closing for LiDAR-based SLAM
Xieyuanli Chen, Thomas Läbe, Andres Milioto +5
Simultaneous localization and mapping (SLAM) is a fundamental capability required by most autonomous systems. In this paper, we address the problem of loop closing for SLAM based o…
SuMa++: Efficient LiDAR-based Semantic SLAM
Xieyuanli Chen, Andres Milioto, Emanuele Palazzolo +3
Reliable and accurate localization and mapping are key components of most autonomous systems. Besides geometric information about the mapped environment, the semantics plays an imp…
Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data
Xieyuanli Chen, Shijie Li, Benedikt Mersch +4
The ability to detect and segment moving objects in a scene is essential for building consistent maps, making future state predictions, avoiding collisions, and planning. In this p…
Visual Place Recognition using LiDAR Intensity Information
Luca Di Giammarino, Irvin Aloise, Cyrill Stachniss +1
Robots and autonomous systems need to know where they are within a map to navigate effectively. Thus, simultaneous localization and mapping or SLAM is a common building block of ro…
4D Panoptic LiDAR Segmentation
Mehmet Aygün, Aljoša Ošep, Mark Weber +4
Temporal semantic scene understanding is critical for self-driving cars or robots operating in dynamic environments. In this paper, we propose 4D panoptic LiDAR segmentation to ass…