Design and Performance Analysis of a Depth Map and Location Prediction Algorithm Based on Recurrent Neural Network Learning

In this paper, we propose the design and performance analysis of a depth mapping and location prediction algorithm optimized for road driving environments with complex geometric features. The proposed algorithm uses a convolutional recurrent cell (ConvLSTM), which combines Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), to predict the depth map and location of the next frame from consecutive image frames. By integrating an Autoencoder for encoding spatial information and an RNN for handling temporal continuity, the algorithm can utilize both spatial and temporal information from visual data for more accurate predictions. The performance of the algorithm was validated using the KITTI vision Benchmark dataset, and the results confirmed that our approach is more efficient than location estimation methods based on complex sensors. These findings indicate that the proposed algorithm can significantly improve performance in understanding the complex spatial structure of road driving environments and estimating location.