Being able to build a map of the environment and to simultaneously localize within this map
is an essential skill for mobile robots navigating in unknown environments in absence of …

In this paper, we propose a survey of the Simultaneous Localization And Mapping (SLAM)
field when considering the recent evolution of autonomous driving. The growing interest …

Autonomous vehicles are expected to play a key role in the future of urban transportation
systems, as they offer potential for additional safety, increased productivity, greater …

For robots to navigate and interact more richly with the world around them, they will likely
require a deeper understanding of the world in which they operate. In robotics and related …

Visual SLAM (simultaneous localization and mapping) refers to the problem of using
images, as the only source of external information, in order to establish the position of a …

Neural RGBD SLAM techniques have shown promise in dense Simultaneous Localization
And Mapping (SLAM) yet face challenges such as error accumulation during camera …

We introduce MIPS-Fusion, a robust and scalable online RGB-D reconstruction method
based on a novel neural implicit representation-multi-implicit-submap. Different from existing …

We present a real-time algorithm which can recover the 3D trajectory of a monocular
camera, moving rapidly through a previously unknown scene. Our system, which we dub …

Recently, Rao-Blackwellized particle filters (RBPF) have been introduced as an effective
means to solve the simultaneous localization and mapping problem. This approach uses a …

The ability to simultaneously localize a robot and accurately map its surroundings is
considered by many to be a key prerequisite of truly autonomous robots. However, few …