AI Concepts LLM typically refers to Large Language Model in the context of artificial intelligence and machine learning.It is a type of deep learning model trained on massive amounts of text data to understand and generate human-like language. Examples: GPT-4, … Continue reading →
Stay tuned for future updates! Types of Localization Types of Frame and Transformation Reference Where is the car with relation to a static reference frame (local or global map)? Where are static objects with relation to the car? Where are … Continue reading →
Posted in Autonomy, SLAM
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Stay tuned for future updates! The Vehicle Interface component provides an interface between Autoware and a vehicle that passes control signals to the vehicle’s drive-by-wire system and receives vehicle information that is passed back to Autoware. Vehicle Lateral Interface Longitudinal … Continue reading →
Stay tuned for future updates! LiDAR Preprocessing High-level Architecture Input Types LiDAR Preprocessing A minimum amount of information is needed to produce correct results. Preprocessing provides sufficient statistics for further algorithms. Remove noisy data from problematic areas Range & angle … Continue reading →
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ROS1 vs ROS2 Architecture ROS2 is well-suited for a variety of applications, including navigation, security, embedded systems, real-time operations, safety-critical tasks, and robotic manipulation. Data Distribution Service (DDS) for ROS2 (1) An industry-standard communication system, a networking middleware (2) Data-Centric … Continue reading →
Stay tuned for future updates! Acceleration and Brake Map Calibration Raw Vehicle Command Converter Steer Offset Estimator Reference Acceleration and Brake Calibration (Polaris GEM Vehicles) accel_brake_map_calibrator Raw Vehicle Command Converter (Polaris GEM Vehicles) Steer Offset Estimator (Polaris GEM Vehicles) Reference … Continue reading →
Stay tuned for future updates! Pure Pursuit Lateral Controller (Kinematic Bicycle Model) Stanley Lateral Controller (Kinematic/Dynamic Bicycle Model) LQR Tracker with Feed Forward Term (Dynamic Bicycle Model) Model Predictive Control (Dynamic Bicycle Model) Coming more, stay tuned for future updates! … Continue reading →
Stay tuned for future updates! Camera Perception Radar Perception LiDAR Perception Sensor Fusion with LiDAR & Camera AutowareAuto High-level Architecture of Perception Component Camera Perception (Temporary Demo, AutowareClass2020) Recap: Camera Model \(D\): aperture diameter \(f\): focal length \(N\): aperture f-number … Continue reading →
ROS2 Software Stack Development Sensing & Data Preprocessing Perception Localization & Mapping (SLAM/V-SLAM) Planning Dynamics & Control Vehicle Interface & PACMod2 Simulation ROS2 Resources ROS2 (Humble) Documentation ROS2 (Humble) Tutorials ROS2 (Humble) Demos ROS2 (Humble) Real-time Programming Autoware.Auto Resources The … Continue reading →
Stay tuned for future detailed updates! Top LiDAR and Front Camera Data Collection and Sensor Calibration Front LiDAR and Front Camera Data Collection and Sensor Calibration Reference [1] Autoware Calibration Toolkit Manual, Link [2] Yan, Guohang, et al. “Opencalib: A … Continue reading →
