GANav: Group-wise Attention Network for Classifying Navigable Regions in Unstructured Outdoor Environments

• URL: http://arxiv.org/abs/2103.04233v1
• Date: Sun, 7 Mar 2021 02:16:24 GMT
• Title: GANav: Group-wise Attention Network for Classifying Navigable Regions in Unstructured Outdoor Environments
• Authors: Tianrui Guan, Divya Kothandaraman, Rohan Chandra and Dinesh Manocha
• Abstract summary: 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.
• Score: 54.21959527308051
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 propose a bottleneck transformer-based deep neural network architecture that uses a novel group-wise attention mechanism to distinguish between navigability levels of different terrains.Our group-wise attention heads enable the network to explicitly focus on the different groups and improve the accuracy. In addition, we propose a dynamic weighted cross entropy loss function to handle the long-tailed nature of the dataset. 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. We compare our approach with prior work on these datasets and achieve an improvement over the state-of-the-art mIoU by 6.74-39.1% on RUGD and 3.82-10.64% on RELLIS-3D.

Related papers

• Boosting Cross-Domain Point Classification via Distilling Relational Priors from 2D Transformers [59.0181939916084]
  Traditional 3D networks mainly focus on local geometric details and ignore the topological structure between local geometries. We propose a novel Priors Distillation (RPD) method to extract priors from the well-trained transformers on massive images. Experiments on the PointDA-10 and the Sim-to-Real datasets verify that the proposed method consistently achieves the state-of-the-art performance of UDA for point cloud classification.
  arXiv  Detail & Related papers  (2024-07-26T06:29:09Z)
• 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)
• Off-Road LiDAR Intensity Based Semantic Segmentation [11.684330305297523]
  Learning-based LiDAR semantic segmentation utilizes machine learning techniques to automatically classify objects in LiDAR point clouds. We address this problem by harnessing the LiDAR intensity parameter to enhance object segmentation in off-road environments. Our approach was evaluated in the RELLIS-3D data set and yielded promising results as a preliminary analysis with improved mIoU for classes "puddle" and "grass"
  arXiv  Detail & Related papers  (2024-01-02T21:27:43Z)
• BEV-DG: Cross-Modal Learning under Bird's-Eye View for Domain Generalization of 3D Semantic Segmentation [59.99683295806698]
  Cross-modal Unsupervised Domain Adaptation (UDA) aims to exploit the complementarity of 2D-3D data to overcome the lack of annotation in a new domain. We propose cross-modal learning under bird's-eye view for Domain Generalization (DG) of 3D semantic segmentation, called BEV-DG.
  arXiv  Detail & Related papers  (2023-08-12T11:09:17Z)
• Semantic Segmentation of Radar Detections using Convolutions on Point Clouds [59.45414406974091]
  We introduce a deep-learning based method to convolve radar detections into point clouds. We adapt this algorithm to radar-specific properties through distance-dependent clustering and pre-processing of input point clouds. Our network outperforms state-of-the-art approaches that are based on PointNet++ on the task of semantic segmentation of radar point clouds.
  arXiv  Detail & Related papers  (2023-05-22T07:09:35Z)
• Subspace Perturbation Analysis for Data-Driven Radar Target Localization [20.34399283905663]
  We use subspace analysis to benchmark radar target localization accuracy across mismatched scenarios. We generate comprehensive datasets by randomly placing targets of variable strengths in mismatched constrained areas. We estimate target locations from these heatmap tensors using a convolutional neural network.
  arXiv  Detail & Related papers  (2023-03-14T21:22:26Z)
• TC-Net: Triple Context Network for Automated Stroke Lesion Segmentation [0.5482532589225552]
  We propose a new network, Triple Context Network (TC-Net), with the capture of spatial contextual information as the core. Our network is evaluated on the open dataset ATLAS, achieving the highest score of 0.594, Hausdorff distance of 27.005 mm, and average symmetry surface distance of 7.137 mm.
  arXiv  Detail & Related papers  (2022-02-28T11:12:16Z)
• Weakly Supervised Change Detection Using Guided Anisotropic Difusion [97.43170678509478]
  We propose original ideas that help us to leverage such datasets in the context of change detection. First, we propose the guided anisotropic diffusion (GAD) algorithm, which improves semantic segmentation results. We then show its potential in two weakly-supervised learning strategies tailored for change detection.
  arXiv  Detail & Related papers  (2021-12-31T10:03:47Z)
• DiResNet: Direction-aware Residual Network for Road Extraction in VHR Remote Sensing Images [12.081877372552606]
  We present a direction-aware residual network (DiResNet) that includes three main contributions. The proposed method has advantages in both overall accuracy and F1-score.
  arXiv  Detail & Related papers  (2020-05-14T19:33:21Z)
• Weakly Supervised Attention Pyramid Convolutional Neural Network for Fine-Grained Visual Classification [71.96618723152487]
  We introduce Attention Pyramid Convolutional Neural Network (AP-CNN) AP-CNN learns both high-level semantic and low-level detailed feature representation. It can be trained end-to-end, without the need of additional bounding box/part annotations.
  arXiv  Detail & Related papers  (2020-02-09T12:33:23Z)

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.