Depth Estimation using Weighted-loss and Transfer Learning

Depth estimation from 2D images is a common computer vision task that has applications in many fields including autonomous vehicles, scene understanding and robotics. The accuracy of a supervised depth estimation method mainly relies on the chosen loss function, the model architecture, quality of data and performance metrics. In this study, we propose a simplified and adaptable approach to improve depth estimation accuracy using transfer learning and an optimized loss function. The optimized loss function is a combination of weighted losses to which enhance robustness and generalization: Mean Absolute Error (MAE), Edge Loss and Structural Similarity Index (SSIM). We use a grid search and a random search method to find optimized weights for the losses, which leads to an improved model. We explore multiple encoder-decoder-based models including DenseNet121, DenseNet169, DenseNet201, and EfficientNet for the supervised depth estimation model on NYU Depth Dataset v2. We observe that the EfficientNet model, pre-trained on ImageNet for classification when used as an encoder, with a simple upsampling decoder, gives the best results in terms of RSME, REL and log10: 0.386, 0.113 and 0.049, respectively. We also perform a qualitative analysis which illustrates that our model produces depth maps that closely resemble ground truth, even in cases where the ground truth is flawed. The results indicate significant improvements in accuracy and robustness, with EfficientNet being the most successful architecture.

Scalable and Efficient Hierarchical Visual Topological Mapping

Hierarchical topological representations can significantly reduce search times within mapping and localization algorithms. Although recent research has shown the potential for such approaches, limited consideration has been given to the suitability and comparative performance of different global feature representations within this context. In this work, we evaluate state-of-the-art hand-crafted and learned global descriptors using a hierarchical topological mapping technique on benchmark datasets and present results of a comprehensive evaluation of the impact of the global descriptor used. Although learned descriptors have been incorporated into place recognition methods to improve retrieval accuracy and enhance overall recall, the problem of scalability and efficiency when applied to longer trajectories has not been adequately addressed in a majority of research studies. Based on our empirical analysis of multiple runs, we identify that continuity and distinctiveness are crucial characteristics for an optimal global descriptor that enable efficient and scalable hierarchical mapping, and present a methodology for quantifying and contrasting these characteristics across different global descriptors. Our study demonstrates that the use of global descriptors based on an unsupervised learned Variational Autoencoder (VAE) excels in these characteristics and achieves significantly lower runtime. It runs on a consumer grade desktop, up to 2.3x faster than the second best global descriptor, NetVLAD, and up to 9.5x faster than the hand-crafted descriptor, PHOG, on the longest track evaluated (St Lucia, 17.6 km), without sacrificing overall recall performance.

Beyond the Known: Adversarial Autoencoders in Novelty Detection

In novelty detection, the goal is to decide if a new data point should be categorized as an inlier or an outlier, given a training dataset that primarily captures the inlier distribution. Recent approaches typically use deep encoder and decoder network frameworks to derive a reconstruction error, and employ this error either to determine a novelty score, or as the basis for a one-class classifier. In this research, we use a similar framework but with a lightweight deep network, and we adopt a probabilistic score with reconstruction error. Our methodology calculates the probability of whether the sample comes from the inlier distribution or not. This work makes two key contributions. The first is that we compute the novelty probability by linearizing the manifold that holds the structure of the inlier distribution. This allows us to interpret how the probability is distributed and can be determined in relation to the local coordinates of the manifold tangent space. The second contribution is that we improve the training protocol for the network. Our results indicate that our approach is effective at learning the target class, and it outperforms recent state-of-the-art methods on several benchmark datasets.

Fisheye Camera and Ultrasonic Sensor Fusion For Near-Field Obstacle Perception in Bird's-Eye-View

Accurate obstacle identification represents a fundamental challenge within the scope of near-field perception for autonomous driving. Conventionally, fisheye cameras are frequently employed for comprehensive surround-view perception, including rear-view obstacle localization. However, the performance of such cameras can significantly deteriorate in low-light conditions, during nighttime, or when subjected to intense sun glare. Conversely, cost-effective sensors like ultrasonic sensors remain largely unaffected under these conditions. Therefore, we present, to our knowledge, the first end-to-end multimodal fusion model tailored for efficient obstacle perception in a bird's-eye-view (BEV) perspective, utilizing fisheye cameras and ultrasonic sensors. Initially, ResNeXt-50 is employed as a set of unimodal encoders to extract features specific to each modality. Subsequently, the feature space associated with the visible spectrum undergoes transformation into BEV. The fusion of these two modalities is facilitated via concatenation. At the same time, the ultrasonic spectrum-based unimodal feature maps pass through content-aware dilated convolution, applied to mitigate the sensor misalignment between two sensors in the fused feature space. Finally, the fused features are utilized by a two-stage semantic occupancy decoder to generate grid-wise predictions for precise obstacle perception. We conduct a systematic investigation to determine the optimal strategy for multimodal fusion of both sensors. We provide insights into our dataset creation procedures, annotation guidelines, and perform a thorough data analysis to ensure adequate coverage of all scenarios. When applied to our dataset, the experimental results underscore the robustness and effectiveness of our proposed multimodal fusion approach.

WoodScape Motion Segmentation for Autonomous Driving -- CVPR 2023 OmniCV Workshop Challenge

Motion segmentation is a complex yet indispensable task in autonomous driving. The challenges introduced by the ego-motion of the cameras, radial distortion in fisheye lenses, and the need for temporal consistency make the task more complicated, rendering traditional and standard Convolutional Neural Network (CNN) approaches less effective. The consequent laborious data labeling, representation of diverse and uncommon scenarios, and extensive data capture requirements underscore the imperative of synthetic data for improving machine learning model performance. To this end, we employ the PD-WoodScape synthetic dataset developed by Parallel Domain, alongside the WoodScape fisheye dataset. Thus, we present the WoodScape fisheye motion segmentation challenge for autonomous driving, held as part of the CVPR 2023 Workshop on Omnidirectional Computer Vision (OmniCV). As one of the first competitions focused on fisheye motion segmentation, we aim to explore and evaluate the potential and impact of utilizing synthetic data in this domain. In this paper, we provide a detailed analysis on the competition which attracted the participation of 112 global teams and a total of 234 submissions. This study delineates the complexities inherent in the task of motion segmentation, emphasizes the significance of fisheye datasets, articulate the necessity for synthetic datasets and the resultant domain gap they engender, outlining the foundational blueprint for devising successful solutions. Subsequently, we delve into the details of the baseline experiments and winning methods evaluating their qualitative and quantitative results, providing with useful insights.

Optimizing Ego Vehicle Trajectory Prediction: The Graph Enhancement Approach

Predicting the trajectory of an ego vehicle is a critical component of autonomous driving systems. Current state-of-the-art methods typically rely on Deep Neural Networks (DNNs) and sequential models to process front-view images for future trajectory prediction. However, these approaches often struggle with perspective issues affecting object features in the scene. To address this, we advocate for the use of Bird's Eye View (BEV) perspectives, which offer unique advantages in capturing spatial relationships and object homogeneity. In our work, we leverage Graph Neural Networks (GNNs) and positional encoding to represent objects in a BEV, achieving competitive performance compared to traditional DNN-based methods. While the BEV-based approach loses some detailed information inherent to front-view images, we balance this by enriching the BEV data by representing it as a graph where relationships between the objects in a scene are captured effectively.

BEVSeg2TP: Surround View Camera Bird's-Eye-View Based Joint Vehicle Segmentation and Ego Vehicle Trajectory Prediction

Trajectory prediction is, naturally, a key task for vehicle autonomy. While the number of traffic rules is limited, the combinations and uncertainties associated with each agent's behaviour in real-world scenarios are nearly impossible to encode. Consequently, there is a growing interest in learning-based trajectory prediction. The proposed method in this paper predicts trajectories by considering perception and trajectory prediction as a unified system. In considering them as unified tasks, we show that there is the potential to improve the performance of perception. To achieve these goals, we present BEVSeg2TP - a surround-view camera bird's-eye-view-based joint vehicle segmentation and ego vehicle trajectory prediction system for autonomous vehicles. The proposed system uses a network trained on multiple camera views. The images are transformed using several deep learning techniques to perform semantic segmentation of objects, including other vehicles, in the scene. The segmentation outputs are fused across the camera views to obtain a comprehensive representation of the surrounding vehicles from the bird's-eye-view perspective. The system further predicts the future trajectory of the ego vehicle using a spatiotemporal probabilistic network (STPN) to optimize trajectory prediction. This network leverages information from encoder-decoder transformers and joint vehicle segmentation.

Self-Supervised Online Camera Calibration for Automated Driving and Parking Applications

Camera-based perception systems play a central role in modern autonomous vehicles. These camera based perception algorithms require an accurate calibration to map the real world distances to image pixels. In practice, calibration is a laborious procedure requiring specialised data collection and careful tuning. This process must be repeated whenever the parameters of the camera change, which can be a frequent occurrence in autonomous vehicles. Hence there is a need to calibrate at regular intervals to ensure the camera is accurate. Proposed is a deep learning framework to learn intrinsic and extrinsic calibration of the camera in real time. The framework is self-supervised and doesn't require any labelling or supervision to learn the calibration parameters. The framework learns calibration without the need for any physical targets or to drive the car on special planar surfaces.