UFO: Uncertainty-aware LiDAR-image Fusion for Off-road Semantic Terrain Map Estimation

• URL: http://arxiv.org/abs/2403.02642v1
• Date: Tue, 5 Mar 2024 04:20:03 GMT
• Title: UFO: Uncertainty-aware LiDAR-image Fusion for Off-road Semantic Terrain Map Estimation
• Authors: Ohn Kim, Junwon Seo, Seongyong Ahn, Chong Hui Kim
• Abstract summary: This paper presents a learning-based fusion method for generating dense terrain classification maps in BEV. Our approach enhances the accuracy of semantic maps generated from an RGB image and a single-sweep LiDAR scan.
• Score: 2.048226951354646
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Autonomous off-road navigation requires an accurate semantic understanding of the environment, often converted into a bird's-eye view (BEV) representation for various downstream tasks. While learning-based methods have shown success in generating local semantic terrain maps directly from sensor data, their efficacy in off-road environments is hindered by challenges in accurately representing uncertain terrain features. This paper presents a learning-based fusion method for generating dense terrain classification maps in BEV. By performing LiDAR-image fusion at multiple scales, our approach enhances the accuracy of semantic maps generated from an RGB image and a single-sweep LiDAR scan. Utilizing uncertainty-aware pseudo-labels further enhances the network's ability to learn reliably in off-road environments without requiring precise 3D annotations. By conducting thorough experiments using off-road driving datasets, we demonstrate that our method can improve accuracy in off-road terrains, validating its efficacy in facilitating reliable and safe autonomous navigation in challenging off-road settings.

Related papers

• An Efficient Approach to Generate Safe Drivable Space by LiDAR-Camera-HDmap Fusion [13.451123257796972]
  We propose an accurate and robust perception module for Autonomous Vehicles (AVs) for drivable space extraction. Our work introduces a robust easy-to-generalize perception module that leverages LiDAR, camera, and HD map data fusion. Our approach is tested on a real dataset and its reliability is verified during the daily (including harsh snowy weather) operation of our autonomous shuttle, WATonoBus.
  arXiv  Detail & Related papers  (2024-10-29T17:54:02Z)
• Neural Semantic Map-Learning for Autonomous Vehicles [85.8425492858912]
  We present a mapping system that fuses local submaps gathered from a fleet of vehicles at a central instance to produce a coherent map of the road environment. Our method jointly aligns and merges the noisy and incomplete local submaps using a scene-specific Neural Signed Distance Field. We leverage memory-efficient sparse feature-grids to scale to large areas and introduce a confidence score to model uncertainty in scene reconstruction.
  arXiv  Detail & Related papers  (2024-10-10T10:10:03Z)
• RoadRunner -- Learning Traversability Estimation for Autonomous Off-road Driving [13.101416329887755]
  We present RoadRunner, a framework capable of predicting terrain traversability and an elevation map directly from camera and LiDAR sensor inputs. RoadRunner enables reliable autonomous navigation, by fusing sensory information, handling of uncertainty, and generation of contextually informed predictions. We demonstrate the effectiveness of RoadRunner in enabling safe and reliable off-road navigation at high speeds in multiple real-world driving scenarios through unstructured desert environments.
  arXiv  Detail & Related papers  (2024-02-29T16:47:54Z)
• Pixel to Elevation: Learning to Predict Elevation Maps at Long Range using Images for Autonomous Offroad Navigation [10.898724668444125]
  We present a learning-based approach capable of predicting terrain elevation maps at long-range using only onboard egocentric images in real-time. We experimentally validate the applicability of our proposed approach for autonomous offroad robotic navigation in complex and unstructured terrain.
  arXiv  Detail & Related papers  (2024-01-30T22:37:24Z)
• View Consistent Purification for Accurate Cross-View Localization [59.48131378244399]
  This paper proposes a fine-grained self-localization method for outdoor robotics. The proposed method addresses limitations in existing cross-view localization methods. It is the first sparse visual-only method that enhances perception in dynamic environments.
  arXiv  Detail & Related papers  (2023-08-16T02:51:52Z)
• Language-Guided 3D Object Detection in Point Cloud for Autonomous Driving [91.91552963872596]
  We propose a new multi-modal visual grounding task, termed LiDAR Grounding. It jointly learns the LiDAR-based object detector with the language features and predicts the targeted region directly from the detector. Our work offers a deeper insight into the LiDAR-based grounding task and we expect it presents a promising direction for the autonomous driving community.
  arXiv  Detail & Related papers  (2023-05-25T06:22:10Z)
• Uncertainty-aware Vision-based Metric Cross-view Geolocalization [25.87104194833264]
  We present an end-to-end differentiable model that uses the ground and aerial images to predict a probability distribution over possible vehicle poses. We improve the previous state-of-the-art by a large margin even without ground or aerial data from the test region.
  arXiv  Detail & Related papers  (2022-11-22T10:23:20Z)
• Semantic Image Alignment for Vehicle Localization [111.59616433224662]
  We present a novel approach to vehicle localization in dense semantic maps using semantic segmentation from a monocular camera. In contrast to existing visual localization approaches, the system does not require additional keypoint features, handcrafted localization landmark extractors or expensive LiDAR sensors.
  arXiv  Detail & Related papers  (2021-10-08T14:40:15Z)
• Cycle and Semantic Consistent Adversarial Domain Adaptation for Reducing Simulation-to-Real Domain Shift in LiDAR Bird's Eye View [110.83289076967895]
  We present a BEV domain adaptation method based on CycleGAN that uses prior semantic classification in order to preserve the information of small objects of interest during the domain adaptation process. The quality of the generated BEVs has been evaluated using a state-of-the-art 3D object detection framework at KITTI 3D Object Detection Benchmark.
  arXiv  Detail & Related papers  (2021-04-22T12:47:37Z)
• GANav: Group-wise Attention Network for Classifying Navigable Regions in Unstructured Outdoor Environments [54.21959527308051]
  We present a new learning-based method for identifying safe and navigable regions in off-road terrains and unstructured environments from RGB images. Our approach consists of classifying groups of terrain classes based on their navigability levels using coarse-grained semantic segmentation. We show through extensive evaluations on the RUGD and RELLIS-3D datasets that our learning algorithm improves the accuracy of visual perception in off-road terrains for navigation.
  arXiv  Detail & Related papers  (2021-03-07T02:16:24Z)
• Robust Autonomous Landing of UAV in Non-Cooperative Environments based on Dynamic Time Camera-LiDAR Fusion [11.407952542799526]
  We construct a UAV system equipped with low-cost LiDAR and binocular cameras to realize autonomous landing in non-cooperative environments. Taking advantage of the non-repetitive scanning and high FOV coverage characteristics of LiDAR, we come up with a dynamic time depth completion algorithm. Based on the depth map, the high-level terrain information such as slope, roughness, and the size of the safe area are derived.
  arXiv  Detail & Related papers  (2020-11-27T14:47:02Z)

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.