Mar 15, 2020 · In this paper, we present an end-to-end recurrent neural network that takes available raw sensors as input (IMU, wheel odometry, and motor ...
In this paper, we present an end-to-end recurrent neural network that takes available raw sensors as input (IMU, wheel odometry, and motor currents) and outputs ...
An end-to-end recurrent neural network that takes available raw sensors as input (IMU, wheel odometry, and motor currents), and outputs velocity estimates, ...
Sep 15, 2020 · In this paper, we propose a learning-based method to perform velocity estimation in extreme scenarios like racing (second category) without ...
Nov 16, 2024 · In this letter, we present an end-to-end recurrent neural network that takes available raw sensors as input (IMU, wheel odometry, and motor ...
Aug 28, 2024 · The racing car velocity estimation task can also be solved without using Kalman filtering but by exploiting the RNNs trained in an end-to-end ...
End-to-end velocity estimation for autonomous racing. S Srinivasan, I Sa, A Zyner, V Reijgwart, MI Valls, R Siegwart. IEEE Robotics and Automation Letters 5 (4) ...
End-to-End Velocity Estimation For Autonomous Racing. Sirish Srinivasan ... Redundant Perception and State Estimation for Reliable Autonomous Racing.
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A novel neural network architecture based on Long Short-Term Memory (LSTM) networks is introduced to accurately estimate the vehicle's velocity in different ...
End-to-End Velocity Estimation For Autonomous Racing ... Velocity estimation plays a central role in driverless vehicles, but standard and affordable methods ...