Abstract:
This thesis explores innovative methods and frameworks to enhance intelligent systems' visual perception capabilities. Vision is the primary means by which many animals perceive, understand, learn, reason about, and interact with the world to achieve their goals. Unlike animals, intelligent systems must acquire these capabilities by processing raw visual data captured by cameras using computer vision and deep learning.
First, we consider a crucial aspect of visual perception in intelligent systems: understanding the structure and layout of the environment. To enable applications such as object interaction or extended reality in previously unseen spaces, these systems are often required to estimate their own motion. When operating in novel environments, they must also construct a map of the space. Together, we have the essence of the Simultaneous Localization and Mapping (SLAM) problem. However, pre-mapping environments can be impractical, costly, and unscalable in scenarios like disaster response or home automation. This makes it essential to develop robots capable of autonomously exploring and mapping unknown areas, a process known as Active SLAM.
Active SLAM typically involves a multi-step process in which the robot acts on the available information to decide the next best actions. The goal is to autonomously and efficiently explore environments without using prior information. Despite an extensive history, Active SLAM methods focused only on short- or long-term objectives, without considering the totality of the process or adapting to the ever-changing states. Addressing these gaps, we introduce iRotate to capitalize on continuous information-gain prediction. Distinct from prevailing approaches, iRotate constantly (pre)optimizes camera viewpoints acting on i) long-term, ii) short-term, and iii) real time objectives. By doing this, iRotate significantly reduces energy consumption and localization errors, thus diminishing the exploration effort - a substantial leap in efficiency and effectiveness.
iRotate, like many other SLAM approaches, leverages the assumption of operating in a static environment. Dynamic components in the scene significantly impact SLAM performance in the localization, place recognition, and optimization steps, hindering the widespread adoption of autonomous robots. This stems from the difficulties of collecting diverse ground truth information in the real world and the long-standing limitations of simulation tools. Testing directly in the real world is costly and risky without prior simulation validation. Datasets instead are inherently static and non-interactive making them useless for developing autonomous approaches. Then, existing simulation tools often lack the visual realism and flexibility to create and control fully customized experiments to bridge the gap between simulation and the real world. This thesis addresses the challenges of obtaining ground truth data and simulating dynamic environments by introducing the GRADE framework. Through a photorealistic rendering engine, we enable online and offline testing of robotic systems and the generation of richly annotated synthetic ground truth data. By ensuring flexibility and repeatability, we allow the extension of previous experiments through variations, for example, in scene content or sensor settings.
Synthetic data can first be used to address several challenges in the context of Deep Learning (DL) approaches, e.g. mismatched data distribution between applications, costs and limits of data collection procedures, and errors caused by incorrect or inconsistent labeling in training datasets. However, the gap between the real and simulated worlds often limits the direct use of synthetic data making style transfer, adaptation techniques, or real-world information necessary. Here, we leverage the photorealism obtainable with GRADE to generate synthetic data and overcome these issues. First, since humans are significant sources of dynamic behavior in environments and the target of many applications, we focus on their detection and segmentation. We train models on real, synthetic, and mixed datasets, and show that using only synthetic data can lead to state-of-the-art
performance in indoor scenarios.
Then, we leverage GRADE to benchmark several Dynamic Visual SLAM methods. These often rely on semantic segmentation and optical flow techniques to identify moving objects and exclude their visual features from the pose estimation and optimization processes. Our evaluations show how they tend to reject too many features, leading to failures in accurately and fully tracking camera trajectories. Surprisingly, we observed low tracking rates not only on simulated sequences but also in real-world datasets. Moreover, we also show that the performance of the segmentation and detection models used are not always positively correlated with the ones of the Dynamic Visual SLAM methods. These failures are mainly due to incorrect estimations, crowded scenes, and not considering the different motion states that the object can have. Addressing this, we introduce DynaPix. This Dynamic Visual SLAM method estimates per-pixel motion probabilities and incorporates them into a new enhanced pose estimation and optimization processes within the SLAM backend, resulting in longer tracking times and lower trajectory errors.
Finally, we use GRADE to address the challenge of limited and inaccurate annotations of wild zebras, particularly for their detection and pose estimation when observed by unmanned aerial vehicles. Leveraging the flexibility of GRADE, we introduce ZebraPose - the first full top-down synthetic-to-real detection and 2D pose estimation method. Unlike previous approaches, ZebraPose demonstrates that both tasks can be performed using only synthetic data, eliminating the need for costly data collection campaigns, time-consuming annotation procedures, or syn-to-real transfer techniques. Ultimately, this thesis demonstrates how combining perception with action can overcome critical limitations in robotics and environmental perception, thereby advancing the deployment of intelligent and autonomous systems for real-world applications. Through innovations like iRotate, GRADE, and ZebraPose, it paves the way for more robust, flexible, and efficient intelligent systems capable of navigating dynamic environments.
SLAM