Abstract Automated Driving Systems (ADS) open up a new domain for the automotive
industry and offer new possibilities for future transportation with higher efficiency and …

This article surveys recent progress and discusses future opportunities for simultaneous
localization and mapping (SLAM) in extreme underground environments. SLAM in …

This paper reports on the state of the art in underground SLAM by discussing different SLAM
strategies and results across six teams that participated in the three-year-long SubT …

Many existing datasets for lidar place recognition are solely representative of structured
urban environments, and have recently been saturated in performance by deep learning …

Artificial perception for robots operating in outdoor natural environments, including forest
scenarios, has been the object of a substantial amount of research for decades. Regardless …

Navigation in forest environments is a challenging and open problem in the area of field
robotics. Rovers in forest environments are required to infer the traversability of a priori …

Visual and lidar Simultaneous Localization and Mapping (SLAM) algorithms benefit from the
Inertial Measurement Unit (IMU) modality. The high-rate inertial data complement the other …

In this letter, we propose a way to model the resilience of the Iterative Closest Point (ICP)
algorithm in the presence of corrupted measurements. In the context of autonomous …

We extend the behavior of generic sample-based motion planners to support obstacle
avoidance during long-range path following by introducing a new edge-cost metric paired …

An accurate motion model is a fundamental component of most autonomous navigation
systems. While much work has been done on improving model formulation, no standard …