8 accepted papers
Localization is one of the core parts of modern robotics. Classic localization methods typically follow the retrieve-then-register paradigm, achieving remarkable success. Recently, the emergence of end-to-end localization approaches has offered distinct advantages, including a streamlined system arc
Point cloud prediction (PCP) aims to forecast future 3D point clouds of scenes by leveraging sequential historical LiDAR scans, offering a promising avenue to enhance the perceptual capabilities of autonomous systems. However, existing methods mostly adopt an end-to-end approach without explicitly m
Accurate and robust simultaneous localization and mapping (SLAM) is crucial for autonomous mobile systems, typically achieved by leveraging the geometric features of the environment. Incorporating semantics provides a richer scene representation that not only enhances localization accuracy in SLAM b
Scene flow estimation (SFE) and motion segmentation (MOS) using 4D radar are emerging yet challenging tasks in robotics and autonomous driving applications. Existing LiDAR- or RGB-D-based point cloud processing methods often deliver suboptimal performance on radar data due to radar signals' highly s
The millimeter-wave radar sensor maintains stable performance under adverse environmental conditions, making it a promising solution for all-weather perception tasks, such as outdoor mobile robotics. However, the radar point clouds are relatively sparse and contain massive ghost points, which greatl…
Loop closing is a crucial component in SLAM that helps eliminate accumulated errors through two main steps: loop detection and loop pose correction. The first step determines whether loop closing should be performed, while the second estimates the 6-DoF pose to correct odometry drift. Current method
Identifying moving objects is a crucial capability for autonomous navigation, consistent map generation, and future trajectory prediction of objects. In this paper, we propose a novel network that addresses the challenge of segmenting moving objects in 3D LiDAR scans. Our approach not only predicts…
In this paper, we consider designing a fall-back mechanism in an interference-aware receiver. Typically, there are two types of detectors dealing with interference, known as enhanced interference rejection combining (eIRC) and symbol-level interference cancellation (SLIC). Although a SLIC detector p…