Improving Perception via Sensor Placement: Designing Multi-LiDAR Systems for Autonomous Vehicles

• URL: http://arxiv.org/abs/2105.00373v1
• Date: Sun, 2 May 2021 01:52:18 GMT
• Title: Improving Perception via Sensor Placement: Designing Multi-LiDAR Systems for Autonomous Vehicles
• Authors: Sharad Chitlangia, Zuxin Liu, Akhil Agnihotri, Ding Zhao
• Abstract summary: We propose an easy-to-compute information-theoretic surrogate cost metric based on Probabilistic Occupancy Grids (POG) to optimize LiDAR placement for maximal sensing. Our results confirm that sensor placement is an important factor in 3D point cloud-based object detection and could lead to a variation of performance by 10% 20% on the state-of-the-art perception algorithms.
• Score: 16.45799795374353
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent years have witnessed an increasing interest in improving the perception performance of LiDARs on autonomous vehicles. While most of the existing works focus on developing novel model architectures to process point cloud data, we study the problem from an optimal sensing perspective. To this end, together with a fast evaluation function based on ray tracing within the perception region of a LiDAR configuration, we propose an easy-to-compute information-theoretic surrogate cost metric based on Probabilistic Occupancy Grids (POG) to optimize LiDAR placement for maximal sensing. We show a correlation between our surrogate function and common object detection performance metrics. We demonstrate the efficacy of our approach by verifying our results in a robust and reproducible data collection and extraction framework based on the CARLA simulator. Our results confirm that sensor placement is an important factor in 3D point cloud-based object detection and could lead to a variation of performance by 10% ~ 20% on the state-of-the-art perception algorithms. We believe that this is one of the first studies to use LiDAR placement to improve the performance of perception.

Related papers

• TLD-READY: Traffic Light Detection -- Relevance Estimation and Deployment Analysis [9.458657306918859]
  Effective traffic light detection is a critical component of the perception stack in autonomous vehicles. This work introduces a novel deep-learning detection system while addressing the challenges of previous work. We propose a relevance estimation system that innovatively uses directional arrow markings on the road, eliminating the need for prior map creation.
  arXiv  Detail & Related papers  (2024-09-11T14:12:44Z)
• Study of Dropout in PointPillars with 3D Object Detection [0.0]
  3D object detection is critical for autonomous driving, leveraging deep learning techniques to interpret LiDAR data. This study provides an analysis of enhancing the performance of PointPillars model under various dropout rates.
  arXiv  Detail & Related papers  (2024-09-01T09:30:54Z)
• 4D Contrastive Superflows are Dense 3D Representation Learners [62.433137130087445]
  We introduce SuperFlow, a novel framework designed to harness consecutive LiDAR-camera pairs for establishing pretraining objectives. To further boost learning efficiency, we incorporate a plug-and-play view consistency module that enhances alignment of the knowledge distilled from camera views.
  arXiv  Detail & Related papers  (2024-07-08T17:59:54Z)
• Multi-Modal Data-Efficient 3D Scene Understanding for Autonomous Driving [58.16024314532443]
  We introduce LaserMix++, a framework that integrates laser beam manipulations from disparate LiDAR scans and incorporates LiDAR-camera correspondences to assist data-efficient learning. Results demonstrate that LaserMix++ outperforms fully supervised alternatives, achieving comparable accuracy with five times fewer annotations. This substantial advancement underscores the potential of semi-supervised approaches in reducing the reliance on extensive labeled data in LiDAR-based 3D scene understanding systems.
  arXiv  Detail & Related papers  (2024-05-08T17:59:53Z)
• ParisLuco3D: A high-quality target dataset for domain generalization of LiDAR perception [4.268591926288843]
  This paper provides a novel dataset, ParisLuco3D, specifically designed for cross-domain evaluation. Online benchmarks for LiDAR semantic segmentation, LiDAR object detection, and LiDAR tracking are provided.
  arXiv  Detail & Related papers  (2023-10-25T10:45:38Z)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• Improving LiDAR 3D Object Detection via Range-based Point Cloud Density Optimization [13.727464375608765]
  Existing 3D object detectors tend to perform well on the point cloud regions closer to the LiDAR sensor as opposed to on regions that are farther away. We observe that there is a learning bias in detection models towards the dense objects near the sensor and show that the detection performance can be improved by simply manipulating the input point cloud density at different distance ranges.
  arXiv  Detail & Related papers  (2023-06-09T04:11:43Z)
• Analyzing Infrastructure LiDAR Placement with Realistic LiDAR [14.163886343824064]
  Vehicle-to-Everything(V2X) cooperative perception has attracted increasing attention. How to find the optimal placement of infrastructure sensors is rarely studied. We propose a pipeline that can efficiently and effectively find optimal installation positions for infrastructure sensors.
  arXiv  Detail & Related papers  (2022-11-29T07:18:32Z)
• Learning to Simulate Realistic LiDARs [66.7519667383175]
  We introduce a pipeline for data-driven simulation of a realistic LiDAR sensor. We show that our model can learn to encode realistic effects such as dropped points on transparent surfaces. We use our technique to learn models of two distinct LiDAR sensors and use them to improve simulated LiDAR data accordingly.
  arXiv  Detail & Related papers  (2022-09-22T13:12:54Z)
• AGO-Net: Association-Guided 3D Point Cloud Object Detection Network [86.10213302724085]
  We propose a novel 3D detection framework that associates intact features for objects via domain adaptation. We achieve new state-of-the-art performance on the KITTI 3D detection benchmark in both accuracy and speed.
  arXiv  Detail & Related papers  (2022-08-24T16:54:38Z)
• Efficient and Robust LiDAR-Based End-to-End Navigation [132.52661670308606]
  We present an efficient and robust LiDAR-based end-to-end navigation framework. We propose Fast-LiDARNet that is based on sparse convolution kernel optimization and hardware-aware model design. We then propose Hybrid Evidential Fusion that directly estimates the uncertainty of the prediction from only a single forward pass.
  arXiv  Detail & Related papers  (2021-05-20T17:52:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.