Autonomous driving vehicles require both a precise localization and map** solution in
different driving environment. In this context, Simultaneous Localization and Map** …

SLAM is an abbreviation for simultaneous localization and map**, which is a technique for
estimating sensor motion and reconstructing structure in an unknown environment …

Dense simultaneous localization and map** (SLAM) is crucial for robotics and augmented
reality applications. However current methods are often hampered by the non-volumetric or …

The ability to predict depth from a single image-using recent advances in CNNs-is of
increasing interest to the vision community. Unsupervised strategies to learning are …

We present an algorithm for tightly aligning two colored point clouds. The key idea is to
optimize a joint photometric and geometric objective that locks the alignment along both the …

Estimating a scene reconstruction and the camera motion from in-body videos is challenging
due to several factors, eg the deformation of in-body cavities or the lack of texture. In this …

This paper introduces a network architecture to solve the structure-from-motion (SfM)
problem via feature-metric bundle adjustment (BA), which explicitly enforces multi-view …

Accurate localization of a vehicle is a fundamental challenge and one of the most important
tasks of mobile robots. For autonomous navigation, motion tracking, and obstacle detection …

We introduce the Imperial College London and National University of Ireland Maynooth (ICL-
NUIM) dataset for the evaluation of visual odometry, 3D reconstruction and SLAM algorithms …