We propose a dense neural simultaneous localization and mapping (SLAM) approach for
monocular RGBD input which anchors the features of a neural scene representation in a …

Visual Odometry (VO) plays a pivotal role in autonomous systems, with a principal challenge
being the lack of depth information in camera images. This paper introduces OCC-VO, a …

Goal-driven mobile robot navigation in map-less environments requires effective state
representations for reliable decision-making. Inspired by the favorable properties of Bird's …

Significant advances in robotics and machine learning have resulted in many datasets
designed to support research into autonomous vehicle technology. However, these datasets …

Autonomous surface vessels (ASVs) are growing rapidly due to their ability to execute
hazardous and time-consuming missions over water surfaces. Recently, camera-based …

In recent years, the significance of unmanned surface vehicles (USVs) has grown
substantially across a wide range of applications. Monocular cameras, as the most common …

Neural implicit representations have recently revolutionized simultaneous localization and
mapping (SLAM), giving rise to a groundbreaking paradigm known as NeRF-based SLAM …

Birds-Eye-View (BEV) segmentation aims to establish a spatial mapping from the
perspective view to the top view and estimate the semantic maps from monocular images …

Monocular visual odometry (MVO) is vital in autonomous navigation and robotics, providing
a cost-effective and flexible motion tracking solution, but the inherent scale ambiguity in …

Bird's-Eye-View (BEV) segmentation is a essential technology for safe and efficient
navigation. This is even more necessary in indoor driving, where there are dynamic and …