EdgeLPR: On the Deep Neural Network trade-off between Precision and Performance in LiDAR Place Recognition

• URL: http://arxiv.org/abs/2605.02275v1
• Date: Mon, 04 May 2026 06:52:14 GMT
• Title: EdgeLPR: On the Deep Neural Network trade-off between Precision and Performance in LiDAR Place Recognition
• Abstract summary: This work explores efficient LiDAR-based place recognition for EdgeAI by leveraging Bird's Eye View representations.<n>Experiments reveal trade-offs between accuracy, robustness, and efficiency.<n>Results are a strong basis for future research on 'use-case'-aware quantisation of Neural Networks for Edge deployment.
• Score: 1.8005402526211627
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Place recognition is essential for long-term autonomous navigation, enabling loop closure and consistent mapping. Although deep learning has improved performance, deploying such models on resource-constrained platforms remains challenging. This work explores efficient LiDAR-based place recognition for EdgeAI by leveraging Bird's Eye View representations to enable lightweight image-based networks. We benchmark representative architectures without aggregation heads using a unified descriptor scheme based on global pooling and linear projection, and evaluate performance under FP32, FP16, and INT8 quantization. Experiments reveal trade-offs between accuracy, robustness, and efficiency: FP16 matches FP32 with lower cost, while INT8 introduces architecture-dependent degradation. Overall, the presented results are a strong basis for future research on 'use-case'-aware quantisation of Neural Networks for Edge deployment.

Related papers

• Same Predictions, Different Reasons: The Effect of Quantization on Model Explanations [0.431493144418712]
  Post-training quantization (PTQ) has become a practical solution for deploying deep learning models on resource-constrained edge devices.<n>This study presents a systematic evaluation on how static PTQ affects the interpretability / explainability of five widely used CNN architectures.
  arXiv  Detail & Related papers  (2026-07-24T19:26:47Z)
• Efficient Hybrid CNN-GNN Architecture for Monocular Depth Estimation [0.0]
  We present GraphDepth, a monocular depth estimation architecture that integrates Graph Neural Networks (GNNs) within a convolutional encoder-decoder framework.<n>Our approach embeds efficient GraphSAGE layers at multiple scales of a ResNet-101 U-Net backbone.<n> Experiments on NYU Depth V2, WHU Aerial, ETH3D, and Mid-Air benchmarks demonstrate competitive accuracy within 4.6% of state-of-the-art transformers.
  arXiv  Detail & Related papers  (2026-05-11T09:21:04Z)
• SlimEdge: Lightweight Distributed DNN Deployment on Constrained Hardware [0.6219985442687116]
  Deep distributed networks (DNNs) have become central to modern computer vision.<n>Our method integrates a structured model pruning with a multi-objective optimization to tailor network capacity to heterogeneous device constraints.<n>Results show that the resulting models satisfy user-specified bounds on accuracy and memory footprint while reducing inference latency by factors ranging from 1.2x to 5.0x.
  arXiv  Detail & Related papers  (2025-12-11T04:02:21Z)
• FEDONet : Fourier-Embedded DeepONet for Spectrally Accurate Operator Learning [0.21847754147782886]
  DeepONets have emerged as powerful data-driven frameworks for learning nonlinear operators.<n>FEDONet demonstrates superior performance compared to the traditional DeepONet.<n>FEDONet delivers consistently superior reconstruction accuracy across all benchmark PDEs.
  arXiv  Detail & Related papers  (2025-09-15T18:13:28Z)
• Efficient Memristive Spiking Neural Networks Architecture with Supervised In-Situ STDP Method [0.0]
  Memristor-based Spiking Neural Networks (SNNs) with temporal spike encoding enable ultra-low-energy computation.<n>This paper presents a circuit-level memristive spiking neural network (SNN) architecture trained using a proposed novel supervised in-situ learning algorithm.
  arXiv  Detail & Related papers  (2025-07-28T17:09:48Z)
• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• Performance Analysis of Various EfficientNet Based U-Net++ Architecture for Automatic Building Extraction from High Resolution Satellite Images [0.0]
  Building extraction heavily relies on semantic segmentation of high-resolution remote sensing imagery. Various efficientNet backbone based U-Net++ has been proposed in this study. According on the experimental findings, the suggested model significantly outperforms previous cutting-edge approaches.
  arXiv  Detail & Related papers  (2023-09-05T18:14:14Z)
• SmoothNets: Optimizing CNN architecture design for differentially private deep learning [69.10072367807095]
  DPSGD requires clipping and noising of per-sample gradients. This introduces a reduction in model utility compared to non-private training. We distilled a new model architecture termed SmoothNet, which is characterised by increased robustness to the challenges of DP-SGD training.
  arXiv  Detail & Related papers  (2022-05-09T07:51:54Z)
• FxP-QNet: A Post-Training Quantizer for the Design of Mixed Low-Precision DNNs with Dynamic Fixed-Point Representation [2.4149105714758545]
  We propose a novel framework referred to as the Fixed-Point Quantizer of deep neural Networks (FxP-QNet) FxP-QNet adapts the quantization level for each data-structure of each layer based on the trade-off between the network accuracy and the low-precision requirements. Results show that FxP-QNet-quantized AlexNet, VGG-16, and ResNet-18 reduce the overall memory requirements of their full-precision counterparts by 7.16x, 10.36x, and 6.44x with less than 0.95%, 0.95%, and 1.99%
  arXiv  Detail & Related papers  (2022-03-22T23:01:43Z)
• Deep Structured Instance Graph for Distilling Object Detectors [82.16270736573176]
  We present a simple knowledge structure to exploit and encode information inside the detection system to facilitate detector knowledge distillation. We achieve new state-of-the-art results on the challenging COCO object detection task with diverse student-teacher pairs on both one- and two-stage detectors.
  arXiv  Detail & Related papers  (2021-09-27T08:26:00Z)
• DAIS: Automatic Channel Pruning via Differentiable Annealing Indicator Search [55.164053971213576]
  convolutional neural network has achieved great success in fulfilling computer vision tasks despite large computation overhead. Structured (channel) pruning is usually applied to reduce the model redundancy while preserving the network structure. Existing structured pruning methods require hand-crafted rules which may lead to tremendous pruning space.
  arXiv  Detail & Related papers  (2020-11-04T07:43:01Z)
• ResNeSt: Split-Attention Networks [86.25490825631763]
  We present a modularized architecture, which applies the channel-wise attention on different network branches to leverage their success in capturing cross-feature interactions and learning diverse representations. Our model, named ResNeSt, outperforms EfficientNet in accuracy and latency trade-off on image classification.
  arXiv  Detail & Related papers  (2020-04-19T20:40:31Z)
• Resolution Adaptive Networks for Efficient Inference [53.04907454606711]
  We propose a novel Resolution Adaptive Network (RANet), which is inspired by the intuition that low-resolution representations are sufficient for classifying "easy" inputs. In RANet, the input images are first routed to a lightweight sub-network that efficiently extracts low-resolution representations. High-resolution paths in the network maintain the capability to recognize the "hard" samples.
  arXiv  Detail & Related papers  (2020-03-16T16:54:36Z)

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.