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Found 6 papers, 2 papers with code
RoadRunner M&M -- Learning Multi-range Multi-resolution Traversability Maps for Autonomous Off-road Navigation
RoadRunner M&M achieves a significant improvement of up to 50% for elevation mapping and 30% for traversability estimation over RoadRunner, and is able to predict in 30% more regions compared to X-Racer while achieving real-time performance.
GlORIE-SLAM: Globally Optimized RGB-only Implicit Encoding Point Cloud SLAM
To alleviate this issue, with the aid of a monocular depth estimator, we introduce a novel DSPO layer for bundle adjustment which optimizes the pose and depth of keyframes along with the scale of the monocular depth.
RoadRunner -- Learning Traversability Estimation for Autonomous Off-road Driving
Furthermore, RoadRunner improves the system latency by a factor of roughly 4, from 500 ms to 140 ms, while improving the accuracy for traversability costs and elevation map predictions.
COVINS-G: A Generic Back-end for Collaborative Visual-Inertial SLAM
The paradigm of a centralized architecture is well established, with the robots (i. e. agents) running Visual-Inertial Odometry (VIO) onboard while communicating relevant data, such as e. g. Keyframes (KFs), to a central back-end (i. e. server), which then merges and optimizes the joint maps of the agents.
LiDAR-guided object search and detection in Subterranean Environments
Detecting objects of interest, such as human survivors, safety equipment, and structure access points, is critical to any search-and-rescue operation.
A Study on the Efficiency of the Indian Stock Market
This study analyzes the weak form efficiency of the Indian Stock market based on the two major Indian stock exchanges, viz., BSE and NSE.
