volume: issue, issue:
Visual Place Recognition (VPR) enables robots to determine current location by comparing input image against previously stored reference images. It is essential in autonomous location and simultaneous localization and mapping (SLAM). A key task of VPR is evaluating similarity between images, as state-of-the-art deep learning-based approaches have achieved outstanding performance in standard indoor/outdoor scenes. However, the SOTA deep learning-based methods underperform in forestry robotic owing to two challenges, constrained computational capabilities and appearance variation due to seasonal shifts, weather/light/viewpoint variations, which substantially impair visual similarity computation. Consequently, this work proposes ForestsNet, a novel lightweight VPR network, to resolve this issue. First, a Binary Neural Network (BNN) was constructed to achieve considerable memory reduction. A novel binarization function, Leaky Sign, is proposed; it adaptively applies quantization factors to input activations, it retains richer feature information during binarization while significantly reducing accuracy degradation of place recognition. Second, Mixer Forests, a novel multi-layer perceptron-based aggregation method is introduced to integrate global context into feature maps, substantially enhancing the robustness against appearance variation. In addition, two novel evaluation metrics, Memory Allocation Efficiency and Balance Compression Recall, are designed to quantify the trade-off between memory efficiency and place recognition accuracy. Experimental results demonstrate that ForestsNet achieves substantially higher memory usage efficiency than full-precision networks. Compared to state-of-the-art BNNs, it presents superior performance in both memory efficiency and place recognition accuracy, establishing itself as a robust VPR solution for resource-constrained forestry robots.
volume: 47, issue: 2
Visual Place Recognition (VPR) enables robots to determine current location by comparing input image against previously stored reference images. It is essential in autonomous location and simultaneous localization and mapping (SLAM). A key task of VPR is evaluating similarity between images, as state-of-the-art deep learning-based approaches have achieved outstanding performance in standard indoor/outdoor scenes. However, the SOTA deep learning-based methods underperform in forestry robotic owing to two challenges, constrained computational capabilities and appearance variation due to seasonal shifts, weather/light/viewpoint variations, which substantially impair visual similarity computation. Consequently, this work proposes ForestsNet, a novel lightweight VPR network, to resolve this issue. First, a Binary Neural Network (BNN) was constructed to achieve considerable memory reduction. A novel binarization function, Leaky Sign, is proposed; it adaptively applies quantization factors to input activations, it retains richer feature information during binarization while significantly reducing accuracy degradation of place recognition. Second, Mixer Forests, a novel multi-layer perceptron-based aggregation method is introduced to integrate global context into feature maps, substantially enhancing the robustness against appearance variation. In addition, two novel evaluation metrics, Memory Allocation Efficiency and Balance Compression Recall, are designed to quantify the trade-off between memory efficiency and place recognition accuracy. Experimental results demonstrate that ForestsNet achieves substantially higher memory usage efficiency than full-precision networks. Compared to state-of-the-art BNNs, it presents superior performance in both memory efficiency and place recognition accuracy, establishing itself as a robust VPR solution for resource-constrained forestry robots.
