Daniel Maturana

Daniel Maturana
Carnegie Mellon University | CMU · Robotics Institute

About

22
Publications
13,081
Reads
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4,348
Citations
Citations since 2016
9 Research Items
3953 Citations
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20162017201820192020202120220200400600
20162017201820192020202120220200400600

Publications

Publications (22)
Preprint
Predicting the motion of a mobile agent from a third-person perspective is an important component for many robotics applications, such as autonomous navigation and tracking. With accurate motion prediction of other agents, robots can plan for more intelligent behaviors to achieve specified objectives, instead of acting in a purely reactive way. Pre...
Chapter
In this paper we describe a semantic mapping system for autonomous off-road driving with an All-Terrain Vehicle (ATVs). The system’s goal is to provide a richer representation of the environment than a purely geometric map, allowing it to distinguish, e.g., tall grass from obstacles. The system builds a 2.5D grid map encoding both geometric (terrai...
Chapter
Semantic scene understanding is a useful capability for autonomous vehicles operating in off-roads. While cameras are the most common sensor used for semantic classification, the performance of methods using camera imagery may suffer when there is significant variation between the train and testing sets caused by illumination, weather, and seasonal...
Chapter
RGB-D cameras provide both a color image and per-pixel depth estimates. The richness of their data and the recent development of low-cost sensors have combined to present an attractive opportunity for mobile robotics research. In this paper, we describe a system for visual odometry and mapping using an RGB-D camera, and its application to autonomou...
Chapter
Many applications for robotic systems require the systems to traverse diverse, unstructured environments. State estimation with Visual Odometry (VO) in these applications is challenging because there is no single algorithm that performs well across all environments and situations. The unique trade-offs inherent to each algorithm mean different algo...
Article
We present a system for the detection of small and potentially obscured obstacles in vegetated terrain. The key novelty of this system is the coupling of a volumetric occupancy map with a 3D Convolutional Neural Network (CNN), which to the best of our knowledge has not been previously done. This architecture allows us to train an extremely efficien...
Article
Full-text available
This paper describes the Tactical Autonomous Aerial LOgistics System (TALOS), developed and flight tested during the first phase of the Office of Naval Research (ONR) Autonomous Aerial Cargo/Utility System (AACUS) Innovative Naval Prototype (INP) program. The ONR vision for AACUS is to create a retrofit perception/planning/human interface system th...
Article
This paper poses object category detection in images as a type of 2D-to-3D alignment problem, utilizing the large quantities of 3D CAD models that have been made publicly available online. Using the 'chair' class as a running example, we propose an exemplar-based 3D category representation, which can explicitly model chairs of different styles as w...
Article
RGB-D cameras provide both color images and per-pixel depth estimates. The richness of this data and the recent development of low-cost sensors have combined to present an attractive opportunity for mobile robotics research. In this paper, we describe a system for visual odometry and mapping using an RGB-D camera, and its application to autonomous...
Article
Full-text available
This paper describes the main activities and achievements of our research group on Machine Intelligence and Robotics (Grima) at the Computer Science Department, Pontificia Universidad Catolica de Chile (PUC). Since 2002, we have been developing an active research in the area of indoor autonomous social robots. Our main focus has been the cognitive...
Conference Paper
Full-text available
Histograms of Local Binary Patterns (LBPs) and variations thereof are a popular local visual descriptor for face recognition. So far, most variations of LBP are designed by hand or are learned with non-supervised methods. In this work we propose a simple method to learn discriminative LBPs in a supervised manner. The method represents an LBP-like d...
Article
Full-text available
RGB-D cameras provide both a color image and per-pixel depth esti-mates. The richness of their data and the recent development of low-cost sensors have combined to present an attractive opportunity for mobile robotics research. In this paper, we describe a system for visual odometry and mapping using an RGB-D camera, and its application to autonomo...
Conference Paper
Full-text available
Many state-of-the-art face recognition algorithms use image descriptors based on features known as Local Binary Patterns (LBPs). While many variations of LBP exist, so far none of them can automatically adapt to the training data. We introduce and analyze a novel generalization of LBP that learns the most discriminative LBP-like features for each f...
Conference Paper
Full-text available
Face recognition algorithms commonly assume that face images are well aligned and have a similar pose -- yet in many practical applications it is impossible to meet these conditions. Therefore extending face recognition to unconstrained face images has become an active area of research. To this end, histograms of Local Binary Patterns (LBP) have pr...
Article
Full-text available
Recent advances in state estimation, planning, map-ping, and control algorithms combined with ever-increasing computational power available onboard micro air vehicles have enabled a number of researchers to demonstrate advanced autonomous capabilities in GPS-denied environments. Much of this work has focused around the use of laser range-finder sen...

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