Autonomous navigation of agricultural robots and vehicles in agricultural environments is a
prerequisite for the accomplishment of various tasks. However, precision navigation of …

We present LINS, a lightweight lidar-inertial state estimator, for real-time ego-motion
estimation. The proposed method enables robust and efficient navigation for ground …

Place recognition is a challenging problem in mobile robotics, especially in unstructured
environments or under viewpoint and illumination changes. Most LiDAR-based methods rely …

Place recognition is a key element of mobile robotics. It can assist with the “wake-up” and
“kidnapped robot” problems, where the robot position needs to be estimated without prior …

We propose a methodology for robust, real-time place recognition using an imaging lidar,
which yields image-quality high-resolution 3D point clouds. Utilizing the intensity readings of …

Localization and mapping are key requirements for autonomous mobile systems to perform
navigation and interaction tasks. Iterative Closest Point (ICP) is widely applied for LiDAR …

We present COIN-LIO, a LiDAR Inertial Odometry pipeline that tightly couples information
from LiDAR intensity with geometry-based point cloud registration. The focus of our work is …

We present MIM (Multi-Layer Intensity Map), a novel 3D object representation for robot
perception and autonomous navigation. MIMs consist of multiple stacked layers of 2D grid …

In this paper, we present a lifelong-learning multisensor system for pedestrian detection in
adverse weather conditions. The proposed method combines two people detection …

We present VAPOR, a novel method for autonomous legged robot navigation in
unstructured, densely vegetated outdoor environments using offline Reinforcement Learning …