Indoor Obstacle Discovery on Reflective Ground via Monocular Camera

• URL: http://arxiv.org/abs/2401.01445v1
• Date: Tue, 2 Jan 2024 22:07:44 GMT
• Title: Indoor Obstacle Discovery on Reflective Ground via Monocular Camera
• Authors: Feng Xue and Yicong Chang and Tianxi Wang and Yu Zhou and Anlong Ming
• Abstract summary: Visual obstacle discovery is a key step towards autonomous navigation of indoor mobile robots. In this paper, we argue that the key to this problem lies in obtaining discriminative features for reflections and obstacles. We introduce a new dataset for Obstacle on Reflective Ground (ORG), which comprises 15 scenes with various ground reflections.
• Score: 21.19387987977164
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual obstacle discovery is a key step towards autonomous navigation of indoor mobile robots. Successful solutions have many applications in multiple scenes. One of the exceptions is the reflective ground. In this case, the reflections on the floor resemble the true world, which confuses the obstacle discovery and leaves navigation unsuccessful. We argue that the key to this problem lies in obtaining discriminative features for reflections and obstacles. Note that obstacle and reflection can be separated by the ground plane in 3D space. With this observation, we firstly introduce a pre-calibration based ground detection scheme that uses robot motion to predict the ground plane. Due to the immunity of robot motion to reflection, this scheme avoids failed ground detection caused by reflection. Given the detected ground, we design a ground-pixel parallax to describe the location of a pixel relative to the ground. Based on this, a unified appearance-geometry feature representation is proposed to describe objects inside rectangular boxes. Eventually, based on segmenting by detection framework, an appearance-geometry fusion regressor is designed to utilize the proposed feature to discover the obstacles. It also prevents our model from concentrating too much on parts of obstacles instead of whole obstacles. For evaluation, we introduce a new dataset for Obstacle on Reflective Ground (ORG), which comprises 15 scenes with various ground reflections, a total of more than 200 image sequences and 3400 RGB images. The pixel-wise annotations of ground and obstacle provide a comparison to our method and other methods. By reducing the misdetection of the reflection, the proposed approach outperforms others. The source code and the dataset will be available at https://github.com/XuefengBUPT/IndoorObstacleDiscovery-RG.

Related papers

• Impact of Surface Reflections in Maritime Obstacle Detection [0.0]
  Maritime obstacle detection aims to detect possible obstacles for autonomous driving of unmanned surface vehicles. Previous works have indicated surface reflections as a source of false positives for object detectors in maritime obstacle detection tasks. We show that surface reflections indeed adversely affect detector performance.
  arXiv  Detail & Related papers  (2024-10-11T10:55:24Z)
• Opening the Black Box of 3D Reconstruction Error Analysis with VECTOR [8.142689309891368]
  VECTOR is a visual analysis tool that improves error inspection for stereo reconstruction. VECTOR was developed in partnership with the Perseverance Mars Rover and Ingenuity Mars Helicopter terrain reconstruction team at the NASA Jet Propulsion Laboratory. We report on how this tool was used to debug and improve terrain reconstruction for the Mars 2020 mission.
  arXiv  Detail & Related papers  (2024-08-07T02:03:32Z)
• Visible and Clear: Finding Tiny Objects in Difference Map [50.54061010335082]
  We introduce a self-reconstruction mechanism in the detection model, and discover the strong correlation between it and the tiny objects. Specifically, we impose a reconstruction head in-between the neck of a detector, constructing a difference map of the reconstructed image and the input, which shows high sensitivity to tiny objects. We further develop a Difference Map Guided Feature Enhancement (DGFE) module to make the tiny feature representation more clear.
  arXiv  Detail & Related papers  (2024-05-18T12:22:26Z)
• 3DRef: 3D Dataset and Benchmark for Reflection Detection in RGB and Lidar Data [0.0]
  This paper introduces the first large-scale 3D reflection detection dataset containing more than 50,000 aligned samples of multi-return Lidar, RGB images, and 2D/3D semantic labels. The proposed dataset advances reflection detection by providing a comprehensive testbed with precise global alignment, multi-modal data, and diverse reflective objects and materials.
  arXiv  Detail & Related papers  (2024-03-11T09:29:44Z)
• Zone Evaluation: Revealing Spatial Bias in Object Detection [69.59295428233844]
  A fundamental limitation of object detectors is that they suffer from "spatial bias" We present a new zone evaluation protocol, which measures the detection performance over zones. For the first time, we provide numerical results, showing that the object detectors perform quite unevenly across the zones.
  arXiv  Detail & Related papers  (2023-10-20T01:44:49Z)
• MonoGAE: Roadside Monocular 3D Object Detection with Ground-Aware Embeddings [29.050983641961658]
  We introduce a novel framework for Roadside Monocular 3D object detection with ground-aware embeddings, named MonoGAE. Our approach demonstrates a substantial performance advantage over all previous monocular 3D object detectors on widely recognized 3D detection benchmarks for roadside cameras.
  arXiv  Detail & Related papers  (2023-09-30T14:52:26Z)
• MonoTDP: Twin Depth Perception for Monocular 3D Object Detection in Adverse Scenes [49.21187418886508]
  This paper proposes a monocular 3D detection model designed to perceive twin depth in adverse scenes, termed MonoTDP. We first introduce an adaptive learning strategy to aid the model in handling uncontrollable weather conditions, significantly resisting degradation caused by various degrading factors. Then, to address the depth/content loss in adverse regions, we propose a novel twin depth perception module that simultaneously estimates scene and object depth.
  arXiv  Detail & Related papers  (2023-05-18T13:42:02Z)
• Geometric-aware Pretraining for Vision-centric 3D Object Detection [77.7979088689944]
  We propose a novel geometric-aware pretraining framework called GAPretrain. GAPretrain serves as a plug-and-play solution that can be flexibly applied to multiple state-of-the-art detectors. We achieve 46.2 mAP and 55.5 NDS on the nuScenes val set using the BEVFormer method, with a gain of 2.7 and 2.1 points, respectively.
  arXiv  Detail & Related papers  (2023-04-06T14:33:05Z)
• DORT: Modeling Dynamic Objects in Recurrent for Multi-Camera 3D Object Detection and Tracking [67.34803048690428]
  We propose to model Dynamic Objects in RecurrenT (DORT) to tackle this problem. DORT extracts object-wise local volumes for motion estimation that also alleviates the heavy computational burden. It is flexible and practical that can be plugged into most camera-based 3D object detectors.
  arXiv  Detail & Related papers  (2023-03-29T12:33:55Z)
• Det6D: A Ground-Aware Full-Pose 3D Object Detector for Improving Terrain Robustness [1.4620086904601473]
  We propose Det6D, the first full-degree-of-freedom 3D object detector without spatial and postural limitations. To predict full-degree poses, including pitch and roll, we design a ground-aware orientation branch. Experiments on various datasets demonstrate the effectiveness and robustness of our method in different terrains.
  arXiv  Detail & Related papers  (2022-07-19T17:12:48Z)
• Embracing Single Stride 3D Object Detector with Sparse Transformer [63.179720817019096]
  In LiDAR-based 3D object detection for autonomous driving, the ratio of the object size to input scene size is significantly smaller compared to 2D detection cases. Many 3D detectors directly follow the common practice of 2D detectors, which downsample the feature maps even after quantizing the point clouds. We propose Single-stride Sparse Transformer (SST) to maintain the original resolution from the beginning to the end of the network.
  arXiv  Detail & Related papers  (2021-12-13T02:12:02Z)

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.