Eyes All Around: Design and Analysis of 360-Degree LiDAR Perception Using Equivariant Feature Learning in Unstructured Traffic
Quick Answer
This paper presents a 360-degree LiDAR perception framework for autonomous driving, utilizing rotation equivariant sparse convolutions.
Quick Take
Evaluated on an Ouster OS0 LiDAR dataset in Indian urban traffic, it achieved high detection rates for cars (92.02/90.51) and buses (80.53/76.34), but lower rates for smaller road users like pedestrians (67.45/61.02) and cyclists (73.21/69.54).
Key Points
- Focuses on 360-degree LiDAR perception for complex urban environments.
- Utilizes rotation equivariant feature extraction for improved detection.
- Achieved 92.02% detection rate for cars in dense traffic.
- Lower detection rates for pedestrians (67.45%) and cyclists (73.21%).
- Evaluated on a custom dataset from Indian urban traffic conditions.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2606. 07626v1 Announce Type: new Abstract: Perception in dense, unstructured urban traffic remains a major challenge for autonomous driving because of the wide variety of road users, frequent occlusions, irregular motion patterns, and the lack of standardized road layouts.
Although recent LiDAR based 3D object detectors have shown strong performance in structured driving scenarios, most are developed and evaluated for limited field of view settings, and their behavior under full surround 360-degree sensing is still not well understood. This paper studies a 360-degree LiDAR perception pipeline for autonomous driving, with particular attention to panoramic sensing, azimuthal sector wise spatial processing, and transformation equivariant feature extraction in complex urban scenes.
The paper presents a practical 360-degree perception framework that combines sector wise panoramic processing with rotation equivariant sparse convolutions and evaluates its behavior on a custom Ouster OS0 LiDAR dataset collected across diverse Indian urban traffic conditions. The results show generally stable detection across several object classes, with the strongest performance for cars at 92. 02/90. 51, buses at 80. 53/76. 34, and trucks at 78. 59/74. 16, while lower scores for pedestrians at 67. 45/61.
02, cyclists at 73. 21/69. 54, and motorcyclists at 71. 20/68. 13 reflect the greater difficulty of detecting smaller and more variable road users in dense urban scenes.
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