DeepAI AI Chat
Log In Sign Up

Uncertainty-Aware Tightly-Coupled GPS Fused LIO-SLAM

by   Sabir Hossain, et al.

Delivery robots aim to achieve high precision to facilitate complete autonomy. A precise three-dimensional point cloud map of sidewalk surroundings is required to estimate self-location. With or without the loop closing method, the cumulative error increases gradually after mapping for larger urban or city maps due to sensor drift. Therefore, there is a high risk of using the drifted or misaligned map. This article presented a technique for fusing GPS to update the 3D point cloud and eliminate cumulative error. The proposed method shows outstanding results in quantitative comparison and qualitative evaluation with other existing methods.


page 1

page 4

page 5


Visual Semantic SLAM with Landmarks for Large-Scale Outdoor Environment

Semantic SLAM is an important field in autonomous driving and intelligen...

6-DOF Feature based LIDAR SLAM using ORB Features from Rasterized Images of 3D LIDAR Point Cloud

An accurate and computationally efficient SLAM algorithm is vital for mo...

TUM-FAÇADE: Reviewing and enriching point cloud benchmarks for façade segmentation

Point clouds are widely regarded as one of the best dataset types for ur...

Visual-Inertial SLAM with Tightly-Coupled Dropout-Tolerant GPS Fusion

Robotic applications are continuously striving towards higher levels of ...

PALoc: Robust Prior-assisted Trajectory Generation for Benchmarking

Evaluating simultaneous localization and mapping (SLAM) algorithms neces...

PQM: A Point Quality Evaluation Metric for Dense Maps

LiDAR-based mapping/reconstruction are important for various application...