Lidar-based place recognition for autonomous driving: A survey
LiDAR has gained popularity in autonomous driving due to advantages like long
measurement distance, rich three-dimensional information, and stability in harsh …
measurement distance, rich three-dimensional information, and stability in harsh …
[HTML][HTML] An efficient image-guided-based 3D point cloud moving object segmentation with transformer-attention in autonomous driving
Q Li, Y Zhuang - International Journal of Applied Earth Observation and …, 2023 - Elsevier
For intelligent transportation systems, moving object segmentation (MOS) provides valuable
information for robots and intelligent vehicles, such as collision avoidance, path planning …
information for robots and intelligent vehicles, such as collision avoidance, path planning …
SeqOT: A spatial–temporal transformer network for place recognition using sequential LiDAR data
Place recognition is an important component for autonomous vehicles to achieve loop
closing or global localization. In this article, we tackle the problem of place recognition …
closing or global localization. In this article, we tackle the problem of place recognition …
A survey on global lidar localization: Challenges, advances and open problems
Abstract Knowledge about the own pose is key for all mobile robot applications. Thus pose
estimation is part of the core functionalities of mobile robots. Over the last two decades …
estimation is part of the core functionalities of mobile robots. Over the last two decades …
CVTNet: A cross-view transformer network for LiDAR-based place recognition in autonomous driving environments
LiDAR-based place recognition (LPR) is one of the most crucial components of autonomous
vehicles to identify previously visited places in GPS-denied environments. Most existing LPR …
vehicles to identify previously visited places in GPS-denied environments. Most existing LPR …
Casspr: Cross attention single scan place recognition
Place recognition based on point clouds (LiDAR) is an important component for autonomous
robots or self-driving vehicles. Current SOTA performance is achieved on accumulated …
robots or self-driving vehicles. Current SOTA performance is achieved on accumulated …
BEVPlace: Learning LiDAR-based place recognition using bird's eye view images
Place recognition is a key module for long-term SLAM systems. Current LiDAR-based place
recognition methods usually use representations of point clouds such as unordered points …
recognition methods usually use representations of point clouds such as unordered points …
Tfnet: Exploiting temporal cues for fast and accurate lidar semantic segmentation
LiDAR semantic segmentation plays a crucial role in enabling autonomous driving and
robots to understand their surroundings accurately and robustly. A multitude of methods …
robots to understand their surroundings accurately and robustly. A multitude of methods …