513 citations · 945 across the 9 of their papers we have counts for
9 papers
Radar Velocity Transformer: Single-scan Moving Object Segmentation in Noisy Radar Point Clouds
Matthias Zeller, Vardeep S. Sandhu, Benedikt Mersch +3
The awareness about moving objects in the surroundings of a self-driving vehicle is essential for safe and reliable autonomous navigation. The interpretation of LiDAR and camera da…
Radar Instance Transformer: Reliable Moving Instance Segmentation in Sparse Radar Point Clouds
Matthias Zeller, Vardeep S. Sandhu, Benedikt Mersch +3
The perception of moving objects is crucial for autonomous robots performing collision avoidance in dynamic environments. LiDARs and cameras tremendously enhance scene interpretati…
Building Volumetric Beliefs for Dynamic Environments Exploiting Map-Based Moving Object Segmentation
Benedikt Mersch, Tiziano Guadagnino, Xieyuanli Chen +3
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 r…
KISS-ICP: In Defense of Point-to-Point ICP -- Simple, Accurate, and Robust Registration If Done the Right Way
Ignacio Vizzo, Tiziano Guadagnino, Benedikt Mersch +3
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 ma…
Receding Moving Object Segmentation in 3D LiDAR Data Using Sparse 4D Convolutions
Benedikt Mersch, Xieyuanli Chen, Ignacio Vizzo +3
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…
Automatic Labeling to Generate Training Data for Online LiDAR-based Moving Object Segmentation
Xieyuanli Chen, Benedikt Mersch, Lucas Nunes +4
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 fo…
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…
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…