LiDAR-Based Object Detection and Ground Segmentation for Autonomous Systems
Summary
LiDAR (Light Detection and Ranging) has become a pivotal sensor modality for autonomous systems, offering high-resolution range measurements across diverse environments. By emitting laser pulses and measuring their time of flight, LiDAR generates dense three-dimensional point clouds that underpin perception pipelines in self-driving vehicles, unmanned aerial systems and mobile robots. Object detection frameworks segment these point clouds using clustering, machine learning and deep neural networks to classify vehicles, pedestrians, infrastructure and miscellaneous obstacles. Concurrently, ground segmentation algorithms distinguish traversable surfaces from non-ground returns by applying plane fitting, histogram analysis, region growing or image-based convolutional methods. The seamless integration of object detection with ground segmentation enhances situational awareness, reduces false positives and informs path planning, collision avoidance and semantic mapping. These capabilities are critical for safe operation in urban streets, off-road terrains and complex indoor settings.
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LiDAR-Based Object Detection and Ground Segmentation for Autonomous Systems publication trend
The graph below shows the total number of articles in lidar-based object detection and ground segmentation for autonomous systems across all publications each year (not limited to Nature Index journals).
Technical terms
LiDAR: A remote sensing method that measures distances by illuminating targets with laser light and analysing reflected pulses.
Point cloud: A collection of three-dimensional data points representing the external surfaces of objects and terrain.
Object detection: The process of identifying and localising individual objects within a point cloud using segmentation and classification techniques.
Ground segmentation: The extraction of ground or traversable surfaces from point cloud data by distinguishing ground points from non-ground points.
Range image: A two-dimensional representation of LiDAR data in which pixel values correspond to measured distances or elevation angles.
References
- An Obstacle-Finding Approach for Autonomous Mobile Robots Using 2D LiDAR Data. Big Data and Cognitive Computing (2023).
- Fast Ground Segmentation for 3D LiDAR Point Cloud Based on Jump-Convolution-Process. Remote Sensing (2021).
- A Survey on Ground Segmentation Methods for Automotive LiDAR Sensors. Sensors (2023).
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