Convergence and Consistency Analysis for Extended Kalman Filter Based SLAM

By Shoudong Huang, Gamini Dissanayake. IEEE Transactions on robotics, 2007

440 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/168857807c65675457222dd8efe2541880c3e7b4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Christian E. Pranata
2. Ming Liu
3. Chao Qin
4. Shuyang Zhang
5. J. Han
6. Weisong Shi
7. Jie Tang
8. Liangkai Liu

In Artificial Intelligence

1. Christian E. Pranata
2. Ming Liu
3. Chao Qin
4. Shuyang Zhang
5. J. Han
6. Weisong Shi
7. Liangkai Liu
8. Haoyang Ye

In Electrical and Electronic Engineering

1. Ming Liu
2. Bo Yu
3. Yifan Wang
4. Weisong Shi
5. Jie Tang
6. Shaoshan Liu
7. Liangkai Liu
8. Haoyang Ye

Citations in the corpus

• Edge Computing for Autonomous Driving: Opportunities and Challenges (2019, methodology): “We use Kalman filtering to combine the advantages of the two and provide accurate and real-time position updates [4].”
• Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving (2017, neutral): “progressively, the filter calculating a biased vehicle pose and map [110].”
• LINS: A Lidar-Inertial State Estimator for Robust and Efficient Navigation (2019, background): “It is well known that EKF is vulnerable to linearization errors which may cause poor performance and even lead to divergence [23], [24].”