Learning 1-Bit LiDAR-based Localization with Auxiliary Objective

• URL: http://arxiv.org/abs/2606.27729v1
• Date: Fri, 26 Jun 2026 05:20:35 GMT
• Title: Learning 1-Bit LiDAR-based Localization with Auxiliary Objective
• Abstract summary: 6-DoF LiDAR-based localization is a fundamental capability for autonomous systems operating in large-scale outdoor environments.<n>BiLoc is the first binary neural network framework for 6-DoF LiDAR localization.<n> experiments on large-scale outdoor LiDAR datasets demonstrate that BiLoc establishes a new state of the art for LiDAR localization with BNNs.
• Score: 43.90708001183322
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 6-DoF LiDAR-based localization is a fundamental capability for autonomous systems operating in large-scale outdoor environments. Many deep-learning-based localization methods have achieved promising performance so far. However, as one of the always-on modules competing for limited on-board computational resources, the localization module is expected to consume only a small portion of the overall compute budget. Most existing learning-based methods are still too heavy for this purpose. In contrast, binary neural networks (BNNs) offer an appealing solution, but the 1-bit compression causes severe information loss and performance drop. In this paper, we address this challenge by proposing Binarized LiDAR-based Localization (BiLoc), the first binary neural network framework for 6-DoF LiDAR localization. Specifically, we reinterpret the training of BNNs from the perspective of the information-bottleneck principle, aiming at retaining minimal yet sufficient representations for pose estimation while suppressing redundant variations. And we introduce an auxiliary objective that adaptively regulates information retention in the binary encoder, effectively mitigating the information loss caused by binarization. This auxiliary objective provides additional optimization signals that compensate for the limited representational capacity and the gradient mismatch inherent in BNNs. Extensive experiments on large-scale outdoor LiDAR datasets demonstrate that BiLoc establishes a new state of the art for LiDAR localization with BNNs.

Related papers

• Information-Bottleneck Driven Binary Neural Network for Change Detection [53.866667209237434]
  Binarized Change Detection (BiCD) is the first binary neural network (BNN) designed specifically for change detection.<n>We introduce an auxiliary objective based on the Information Bottleneck (IB) principle, guiding the encoder to retain essential input information.<n>BiCD establishes a new benchmark for BNN-based change detection, achieving state-of-the-art performance in this domain.
  arXiv  Detail & Related papers  (2025-07-04T11:56:16Z)
• BiDense: Binarization for Dense Prediction [62.70804353158387]
  BiDense is a generalized binary neural network (BNN) designed for efficient and accurate dense prediction tasks. BiDense incorporates two key techniques: the Distribution-adaptive Binarizer (DAB) and the Channel-adaptive Full-precision Bypass (CFB)
  arXiv  Detail & Related papers  (2024-11-15T16:46:04Z)
• Enabling On-device Continual Learning with Binary Neural Networks [3.180732240499359]
  We propose a solution that combines recent advancements in the field of Continual Learning (CL) and Binary Neural Networks (BNNs) Specifically, our approach leverages binary latent replay activations and a novel quantization scheme that significantly reduces the number of bits required for gradient computation.
  arXiv  Detail & Related papers  (2024-01-18T11:57:05Z)
• On-Device Learning with Binary Neural Networks [2.7040098749051635]
  We propose a CL solution that embraces the recent advancements in CL field and the efficiency of the Binary Neural Networks (BNN) The choice of a binary network as backbone is essential to meet the constraints of low power devices.
  arXiv  Detail & Related papers  (2023-08-29T13:48:35Z)
• IR2Net: Information Restriction and Information Recovery for Accurate Binary Neural Networks [24.42067007684169]
  Weight and activation binarization can efficiently compress deep neural networks and accelerate model inference, but cause severe accuracy degradation. We propose IR$2$Net to stimulate the potential of BNNs and improve the network accuracy by restricting the input information and recovering the feature information. Experimental results demonstrate that our approach still achieves comparable accuracy even with $ sim $10x floating-point operations (FLOPs) reduction for ResNet-18.
  arXiv  Detail & Related papers  (2022-10-06T02:03:26Z)
• Recurrent Bilinear Optimization for Binary Neural Networks [58.972212365275595]
  BNNs neglect the intrinsic bilinear relationship of real-valued weights and scale factors. Our work is the first attempt to optimize BNNs from the bilinear perspective. We obtain robust RBONNs, which show impressive performance over state-of-the-art BNNs on various models and datasets.
  arXiv  Detail & Related papers  (2022-09-04T06:45:33Z)
• Distribution-sensitive Information Retention for Accurate Binary Neural Network [49.971345958676196]
  We present a novel Distribution-sensitive Information Retention Network (DIR-Net) to retain the information of the forward activations and backward gradients. Our DIR-Net consistently outperforms the SOTA binarization approaches under mainstream and compact architectures. We conduct our DIR-Net on real-world resource-limited devices which achieves 11.1 times storage saving and 5.4 times speedup.
  arXiv  Detail & Related papers  (2021-09-25T10:59:39Z)
• Towards Accurate Knowledge Transfer via Target-awareness Representation Disentanglement [56.40587594647692]
  We propose a novel transfer learning algorithm, introducing the idea of Target-awareness REpresentation Disentanglement (TRED) TRED disentangles the relevant knowledge with respect to the target task from the original source model and used as a regularizer during fine-tuning the target model. Experiments on various real world datasets show that our method stably improves the standard fine-tuning by more than 2% in average.
  arXiv  Detail & Related papers  (2020-10-16T17:45:08Z)

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.