ELDE-Net: Efficient Light-Weight Depth Estimation Network for Deep Reinforcement Learning-Based Mobile Robot Path Planning
Source abstract
Abstract
Precise and robust three-dimensional object detection (3DOD) presents a promising opportunity in the field of mobile robot (MR) navigation. Monocular 3DOD techniques typically involve extending existing two-dimensional object... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Robotic Path Planning Algorithms · Advanced Neural Network Applications · Reinforcement Learning in Robotics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (89)
Show source-provided references
1. A review of sensing technologies for indoor autonomous mobile robots10.3390/s24041222 ↗
2. Multi-scale fully convolutional network-based semantic segmentation for mobile robot navigation10.3390/electronics12030533 ↗
3. Obstacle avoidance strategy for mobile robot based on monocular camera10.3390/electronics12081932 ↗
4. MonoGhost: lightweight monocular GhostNet 3D object properties estimation for autonomous driving10.3390/robotics12060155 ↗
5. IRDC-Net: lightweight semantic segmentation network based on monocular camera for mobile robot navigation10.3390/s23156907 ↗
6. Autonomous aerial obstacle avoidance using LiDAR sensor fusion10.1371/journal.pone.0287177 ↗
7. MonoDTR: monocular 3D object detection with depth-aware transformer
8. MonoDCN: monocular 3D object detection based on dynamic convolution10.1371/journal.pone.0275438 ↗
9. A survey on monocular 3D object detection algorithms based on deep learning10.1088/1742-6596/1518/1/012049 ↗
10. 3D object detection for autonomous driving: a survey
11. A comprehensive RGB-D dataset for 6D pose estimation for industrial robots pick and place: creation and real-world validation
12. A lightweight network for monocular depth estimation with decoupled body and edge supervision10.1016/j.imavis.2021.104261 ↗
13. Unsupervised learning of monocular depth and ego-motion in outdoor/indoor environments
14. GeoNet: unsupervised learning of dense depth, optical flow and camera pose
15. Competitive collaboration: joint unsupervised learning of depth, camera motion, optical flow and motion segmentation
16. Self-supervised monocular depth and visual odometry learning with scale-consistent geometric constraints
17. Deep residual learning for image recognition
18. Unsupervised monocular depth estimation with left-right consistency
19. Optimal obstacle avoidance strategy using deep reinforcement learning based on stereo camera
20. Optimal two-wheeled self-balancing mobile robot strategy of navigation using adaptive fuzzy controller-based KD-SegNet10.1007/s11370-025-00606-0 ↗
21. A deep CNN-based detection method for multi-scale fine-grained objects in remote sensing images10.1109/ACCESS.2024.3356716 ↗
22. Research on modeling and object tracking for robot arm based on deep reinforcement learning
23. Human-level control through deep reinforcement learning10.1038/nature14236 ↗
24. Dynamic path planning of unknown environment based on deep reinforcement learning
25. A novel approach to autonomous driving using double deep Q-network-bsed deep reinforcement learning10.3390/wevj16030138 ↗
26. Intelligent-based multi-robot path planning inspired by improved classical q-learning and improved particle swarm optimization with perturbed velocity
27. Feature point-based 3D tracking of multiple fish from multi-view images10.1371/journal.pone.0180254 ↗
28. Joint 3D proposal generation and object detection from view aggregation
29. PointRCNN: 3D object proposal generation and detection from point cloud
30. SOGDet: semantic-occupancy guided multi-view 3D object detection
31. VoxelNet: end-to-end learning for point cloud-based 3D object detection
32. Voxel R-CNN: towards high performance voxel-based 3D object detection
33. PointPillars: fast encoders for object detection from point clouds
34. SECOND: sparsely embedded convolutional detection10.3390/s18103337 ↗
35. Pseudo-lidar from visual depth estimation: bridging the gap in 3D object detection for autonomous driving
36. Multi-view 3D object detection network for autonomous driving
37. Accurate monocular 3D object detection via color-embedded 3d reconstruction for autonomous driving
38. Monocular 3D object detection via feature domain adaptation
39. Monocular 3D object detection with pseudo-lidar point cloud
40. You Y, Wang Y, Chao WL, Garg D, Pleiss G, Hariharan B, et al. Pseudo-lidar++: accurate depth for 3d object detection in autonomous driving. arXiv:1906.06310. 2019.
41. Monocular 3D object detection via estimation of paired keypoints for autonomous driving
42. 3D bounding box estimation using deep learning and geometry
43. MonoGRNet: a geometric reasoning network for monocular 3D object localization
44. Geometry-based distance decomposition for monocular 3D object detection
45. MonoPair: monocular 3D object detection using pairwise spatial relationships
46. Delving into localization errors for monocular 3D object detection
47. M3D-RPN: monocular 3D region proposal network for object detection
48. MonoSAID: monocular 3D object detection based on scene-level adaptive instance depth estimation10.1007/s10846-023-02027-6 ↗
49. FDE-Net: lightweight depth estimation for monocular cameras
50. Predicting depth, surface normal and semantic labels with a common multi-scale convolutional architecture
51. Estimating depth from monocular images as classification using deep fully convolutional residual networks10.1109/TCSVT.2017.2740321 ↗
52. Unsupervised learning of depth and ego-motion from video
53. Digging into self-supervised monocular depth estimation
54. HR-depth: high resolution self-supervised monocular depth estimation
55. ST-DepthNet: a spatio-temporal deep network for depth completion using a single non-repetitive circular scanning lidar10.1109/LRA.2023.3266670 ↗
56. EfficientNet family U-Net models for deep learning semantic segmentation of kidney tumors on CT images10.3389/fcomp.2023.1235622 ↗
57. Deep machine learning of mobilenet, efficient, and inception models10.3390/a17030096 ↗
58. Shuffenet: an extremely efficient convolutional neural network for mobile devices
59. FastDepth: fast monocular depth estimation on embedded systems
60. Lightweight monocular depth estimation through guided decoding
61. R-MSFM: recurrent multi-scale feature modulation for monocular depth estimating
62. Lite-Mono: a lightweight CNN and transformer architecture for self-supervised monocular depth estimation
63. CondConv: conditionally parameterized convolutions for efficient inference
64. Cheng C, Gao T, Wei S, Du Y, Guo R, Dong S, et al. PP-LCNet: a lightweight CPU convolutional neural network. arXiv:2109.15099. 2021. doi:10.48550/arXiv.2109.15099.
65. Weighted joint bilateral filter with slope depth compensation filter for depth maprefinement
66. IDP-YOLOV9: improvement of object detection model in severe weather scenarios from drone perspective10.3390/app14125277 ↗
67. Monocular depth estimation using a laplacian image pyramid with local planar guidance layers10.3390/s23020845 ↗
68. The cityscapes dataset for semantic urban scene understanding
69. An improved adam optimization algorithm combining adaptive coefficients and composite gradients based on randomized block coordinate descent10.1155/2023/4765891 ↗
70. Brightness contrast-contrast induction model predicts assimilation and inverted assimilation effects10.1167/8.7.27 ↗
71. Zini S, Gomez-Villa A, Buzzelli M, Twardowski B, Bagdanov AD, Van de Weijer J. Planckian Jitter: countering the color-crippling effects of color jitter on self-supervised training. arXiv:2202.07993. 2022. doi:10.48550/arXiv.2202.07993.
72. Scale-, shift- and rotation-invariant diffractive optical networks10.1021/acsphotonics.0c01583 ↗
73. Detect lane line for self-driving car using hue saturation lightness and hue saturation value color transformation10.3991/ijoe.v19i16.43359 ↗
74. KD-SegNet: efficient semantic segmentation network with knowledge distillation based on monocular camera
75. Revisiting single image depth estimation: toward higher resolution maps with accurate object boundaries
76. Structure-aware residual pyramid network for monocular depth estimation
77. Efficient monocular depth estimation for edge devices in internet of things10.1109/TII.2020.3020583 ↗
78. Hybrid mobile robot path planning using safe JBS-A*B algorithm and improved DWA based on monocular camera10.1007/s10846-024-02179-z ↗
79. A lightweight road scene semantic segmentation algorithm
80. Manet: multi-scale aggregated network for light field depth estimation
81. Lightweight monocular depth estimation network for robotics using intercept block GhostNet10.1007/s11760-024-03720-1 ↗
82. Lightweight concrete crack recognition model based on improved MobileNetV310.1038/s41598-025-00468-7 ↗
83. Make3D: learning 3D scene structure from a single still image10.1109/TPAMI.2008.132 ↗
84. MSCA: a multi-scale context-aware recommender system leveraging reviews and user interactions
85. SSR: semi-supervised soft rasterizer for single-view 2D to 3D reconstruction
86. DCF: disparity computing framework for stereo vision systems10.1016/j.simpa.2022.100442 ↗
87. Semi-supervised deep learning for monocular depth map prediction
88. An improved coverage path planning for service robots based on backtracking method
89. Hybrid path planning for mobile robot based on improved A* fusion dynamic window approach