Obstacle perception refers to a system's ability—typically powered by artificial intelligence or robotics—to detect, identify, and locate physical objects that may impede the movement or actions of an autonomous entity (vehicle, robot, drone, etc.). This capability relies on analyzing data from various sensors (cameras, lidars, radars, ultrasonics) and involves real-time processing of complex information to enable appropriate decision-making. It is distinguished from other vision technologies by its specific aim: ensuring safety and autonomy in dynamic and sometimes unpredictable environments.
Use cases and examples
Obstacle perception is essential in autonomous driving, enabling vehicles to detect pedestrians, other vehicles, or objects on the road and adjust their trajectory accordingly. In industry, it is used by mobile robots for navigation in warehouses or production lines. Drones leverage this capability to avoid collisions during flights in urban or natural environments.
Main software tools, libraries, frameworks
Leading tools for obstacle perception include OpenCV (image processing), ROS (Robot Operating System), TensorFlow and PyTorch (deep learning), as well as specialized solutions like NVIDIA Isaac or Baidu Apollo for autonomous vehicles. Sensor data processing frameworks such as PCL (Point Cloud Library) for lidar data are also crucial.
Latest developments, evolutions, and trends
Recent trends involve integrating deep learning to enhance robustness under varying conditions (weather, lighting), using multi-modal sensors to fuse different information sources, and optimizing onboard processing for real-time responsiveness. The rise of self-supervised models and widespread adoption of 3D perception are paving the way for safer and more adaptive systems.