This article presents an accurate, highly efficient, and learning-free method for large-scale
odometry estimation using spinning radar, empirically found to generalize well across very …

This paper presents a radar odometry method that combines probabilistic trajectory
estimation and deep learned features without needing groundtruth pose information. The …

Ego-motion estimation is a fundamental building block of any autonomous system that
needs to navigate in an environment. In large-scale outdoor scenes, 3D LiDARs are often …

Vehicle odometry is an essential component of an automated driving system as it computes
the vehicle's position and orientation. The odometry module has a higher demand and …

We address the problem of learning observation models end-to-end for estimation. Robots
operating in partially observable environments must infer latent states from multiple sensory …

An accurate ego-motion estimation solution is vital for autonomous vehicles. LiDAR is widely
adopted in self-driving systems to obtain depth information directly and eliminate the …

This paper provides an in-depth review of deep learning techniques to address the
challenges of odometry and global ego-localization using frequency modulated continuous …

Point clouds registration and semantic segmentation are two closely linked steps to build
geometrically-accurate and semantically-rich 3D maps. They not only are the fundamental …

We consider the problem of learning error covariance matrices for robotic state estimation.
The convergence of a state estimator to the correct belief over the robot state is dependent …

This paper addresses the self-supervised learning of several critical factors in Visual
Simultaneous Localization and Mapping (VSLAM) in low-level vision: interest point learning …