In this paper, we explore the use of vehicle-to-vehicle (V2V) communication to improve the
perception and motion forecasting performance of self-driving vehicles. By intelligently …

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 …

Robust and accurate, map-based localization is crucial for autonomous mobile systems. In
this paper, we exploit range images generated from 3D LiDAR scans to address the problem …

Localization and mapping are key capabilities of autonomous systems. In this paper, we
propose a modified Siamese network to estimate the similarity between pairs of LiDAR …

This work addresses visual cross-view metric localization for outdoor robotics. Given a
ground-level color image and a satellite patch that contains the local surroundings, the task …

Localization is a crucial capability for mobile robots and autonomous cars. In this paper, we
address learning an observation model for Monte-Carlo localization using 3D LiDAR data …

Over the last few years, we have witnessed tremendous progress on many subtasks of
autonomous driving including perception, motion forecasting, and motion planning …

Most autonomous driving and robotic applications require retrieving map data around the
vehicle's current location. Those maps can cover large areas and are often stored in a …

This paper proposes a fine-grained self-localization method for outdoor robotics that utilizes
a flexible number of onboard cameras and readily accessible satellite images. The …

We propose a novel end-to-end method for cross-view pose estimation. Given a ground-
level query image and an aerial image that covers the query's local neighborhood, the 3 …