Comparison of real-time performance of Kalman filter-based slam methods for unmanned ground vehicle (UGV) navigation

2009 ◽  
Author(s):  
Hakan Temeltaş ◽  
Deniz Kavak
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Unmanned Ground Vehicle ◽  
Ground Vehicle ◽  
Time Performance

Real time prediction and diagnostics for unmanned ground vehicle (UGV) mobility

2007 ◽  
Author(s):  
Holger M. Jaenisch ◽  
James W. Handley ◽  
Michael L. Hicklen
Keyword(s):  
Real Time ◽  
Unmanned Ground Vehicle ◽  
Ground Vehicle ◽  
Time Prediction

A Virtual Unmanned Ground Vehicle Simulator for Verifying the Algorithm based on Real-time Traversability Analysis

2018 ◽  
Vol 24 (4) ◽  
pp. 354-360
Author(s):  
Hajun Song ◽  
Jong-Boo Han ◽  
Hyosung Hong ◽  
Samuel Jung ◽  
Sung-Soo Kim ◽  
...  
Keyword(s):  
Real Time ◽  
Unmanned Ground Vehicle ◽  
Ground Vehicle ◽  
Vehicle Simulator

59225 THE DEVELOPMENT OF A REAL-TIME SIMULATOR FOR UNMANNED GROUND VEHICLE(Control of Multibody Systems)

2010 ◽  
Vol 2010.5 (0) ◽  
pp. _59225-1_-_59225-8_
Author(s):  
Jong Seok Lee ◽  
Jae Yi Oh ◽  
Yeo Giel Yoon ◽  
Ju Yong Kang ◽  
Won Gun Kim ◽  
...  
Keyword(s):  
Real Time ◽  
Multibody Systems ◽  
Vehicle Control ◽  
Unmanned Ground Vehicle ◽  
Ground Vehicle

scholarly journals Multiple Road-Objects Detection and Tracking for Autonomous Driving

2021 ◽  
Author(s):  
Wael Farag ◽  
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
High Performance ◽  
Unscented Kalman Filter ◽  
Measurement Data ◽  
Autonomous Driving ◽  
Estimation Accuracy ◽  
Time Performance ◽  
Detection And Tracking ◽  
Objects Detection

In this paper, a real-time road-Object Detection and Tracking (LR_ODT) method for autonomous driving is proposed. The method is based on the fusion of lidar and radar measurement data, where they are installed on the ego car, and a customized Unscented Kalman Filter (UKF) is employed for their data fusion. The merits of both devices are combined using the proposed fusion approach to precisely provide both pose and velocity information for objects moving in roads around the ego car. Unlike other detection and tracking approaches, the balanced treatment of both pose estimation accuracy and its real-time performance is the main contribution in this work. The proposed technique is implemented using the high-performance language C++ and utilizes highly optimized math and optimization libraries for best real-time performance. Simulation studies have been carried out to evaluate the performance of the LR_ODT for tracking bicycles, cars, and pedestrians. Moreover, the performance of the UKF fusion is compared to that of the Extended Kalman Filter fusion (EKF) showing its superiority. The UKF has outperformed the EKF on all test cases and all the state variable levels (-24% average RMSE). The employed fusion technique show how outstanding is the improvement in tracking performance compared to the use of a single device (-29% RMES with lidar and -38% RMSE with radar).

Nonlinear Model Predictive Controller for an Unmanned Ground Vehicle on Variable Terrain

2015 ◽  
Author(s):  
Andrew Eick ◽  
David Bevly
Keyword(s):  
Real Time ◽  
Nonlinear Model ◽  
Control Algorithm ◽  
Target Location ◽  
Tire Model ◽  
Unmanned Ground Vehicle ◽  
Model Predictive Controller ◽  
Ground Vehicle ◽  
Rollover Risk ◽  
Predictive Controller

Rough, off-road terrain contains multiple hazards for an unmanned ground vehicle (UGV). In this paper, hazards are classified into three groups: obstacles, rough traversable terrain, and rough untraversable terrain. These three types of hazards create a rollover risk for a UGV. A nonlinear model predictive controller (NMPC) that is capable of navigating a UGV through these hazards is presented. The control algorithm features a nonlinear tire model which more accurately captures the dynamics of the UGV when compared to a linearized tire model, and has a fast enough run time for real time implementation. On an actual vehicle, the UGV is assumed to be equipped with a perception based sensor, such as a Light Detection And Ranging (LiDAR) unit, to provide information of the terrain roughness, grade, and elevation. This information is used by the NMPC to safely control the vehicle to a target location. However, for the purposes of this paper, control inputs and terrain are simulated in Car-Sim [1], and the feasibility of real time implementation is investigated.

Kalman-filter-based sensor fusion applied to road-objects detection and tracking for autonomous vehicles

2020 ◽  
pp. 095965182097552
Author(s):  
Wael Farag
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Extended Kalman Filter ◽  
Mean Squared Error ◽  
Unscented Kalman Filter ◽  
Measurement Data ◽  
Root Mean Squared Error ◽  
Time Performance ◽  
Detection And Tracking ◽  
Squared Error

In this article, a real-time road-Object Detection and Tracking (LR_ODT) method for autonomous driving is proposed. This method is based on the fusion of lidar and radar measurement data, where they are installed on the ego car, and a customized Unscented Kalman Filter is employed for their data fusion. The merits of both devices are combined using the proposed fusion approach to precisely provide both pose and velocity information for objects moving in roads around the ego car. Unlike other detection and tracking approaches, the balanced treatment of both pose estimation accuracy and its real-time performance is the main contribution in this work. The proposed technique is implemented using the high-performance language C++ and utilizes highly optimized math and optimization libraries for best real-time performance. Simulation studies have been carried out to evaluate the performance of the LR_ODT for tracking bicycles, cars, and pedestrians. Moreover, the performance of the Unscented Kalman Filter fusion is compared to that of the Extended Kalman Filter fusion showing its superiority. The Unscented Kalman Filter has outperformed the Extended Kalman Filter on all test cases and all the state variable levels (−24% average Root Mean Squared Error). The employed fusion technique shows how outstanding is the improvement in tracking performance compared to the use of a single device (−29% Root Mean Squared Error with lidar and −38% Root Mean Squared Error with radar).

scholarly journals Real‐time keypoints detection for autonomous recovery of the unmanned ground vehicle

2020 ◽  
Vol 14 (17) ◽  
pp. 4690-4700
Author(s):  
Jie Li ◽  
Sheng Zhang ◽  
Kai Han ◽  
Xia Yuan ◽  
Chunxia Zhao ◽  
...  
Keyword(s):  
Real Time ◽  
Unmanned Ground Vehicle ◽  
Ground Vehicle ◽  
Autonomous Recovery ◽  
Keypoints Detection
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