Author name cluster

Çetin Meriçli

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

1 author row

ICRA Conference 2018 Conference Paper

Off-Road Lidar Simulation with Data-Driven Terrain Primitives

Abhijeet Tallavajhula
Çetin Meriçli
Alonzo Kelly

Developing software for large scale off-road robot applications is challenging and tedious due to cost, logistics, and rigor of field testing. High-fidelity sensor-realistic simulation can speed up the development process for perception and state estimation algorithms. We focus on Lidar simulation for robots operating in off-road environments. Lidars are integral sensors for robots, and Lidar simulation for off-road environments is particularly challenging due to the way Lidar rays interact with natural terrain such as vegetation. A hybrid geometric terrain representation has been shown to model Lidar observations well [1]. However, previous work has only been able to simulate a single, fixed scene, and the entire scene had to be precisely surveyed. In this work, we add semantic information to the hybrid geometric model. This allows us to extract terrain primitives, such as trees and shrubs, from data logs. Our approach uses these primitives to compose arbitrary scenes for Lidar simulation. We evaluate our simulator on a real-world environment of interest, and show that primitives derived using our approach generalize to new scenes.

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ICRA Conference 2016 Conference Paper

Slip-aware Model Predictive optimal control for Path following

Venkataramanan Rajagopalan
Çetin Meriçli
Alonzo Kelly

Traditional control and planning algorithms for wheeled mobile robots (WMR) either totally ignore or make simplifying assumptions about the effects of wheel slip on the motion. While this approach works reasonably well in practice on benign terrain, it fails very quickly when the WMR is deployed in terrain that induces significant wheel slip. We contribute a novel control framework that predictively corrects for the wheel slip to effectively minimize path following errors. Our framework, the Receding Horizon Model Predictive Path Follower (RHMPPF), specifically addresses the problem of path following in challenging environments where the wheel slip substantially affects the vehicle mobility. We formulate the solution to the problem as an optimal controller that utilizes a slip-aware model predictive component to effectively correct the controls generated by a strictly geometric pure-pursuit path follower. We present extensive experimental validation of our approach using a simulated 6-wheel skid-steered robot in a high-fidelity data-driven simulator, and on a real 4-wheel skid-steered robot. Our results show substantial improvement in the path following performance in both simulation and real world experiments.

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ICRA Conference 2013 Conference Paper

Fast human detection for indoor mobile robots using depth images

Benjamin Choi
Çetin Meriçli
Joydeep Biswas
Manuela Veloso

A human detection algorithm running on an indoor mobile robot has to address challenges including occlusions due to cluttered environments, changing backgrounds due to the robot's motion, and limited on-board computational resources. We introduce a fast human detection algorithm for mobile robots equipped with depth cameras. First, we segment the raw depth image using a graph-based segmentation algorithm. Next, we apply a set of parameterized heuristics to filter and merge the segmented regions to obtain a set of candidates. Finally, we compute a Histogram of Oriented Depth (HOD) descriptor for each candidate, and test for human presence with a linear SVM. We experimentally evaluate our approach on a publicly available dataset of humans in an open area as well as our own dataset of humans in a cluttered cafe environment. Our algorithm performs comparably well on a single CPU core against another HOD-based algorithm that runs on a GPU even when the number of training examples is decreased by half. We discuss the impact of the number of training examples on performance, and demonstrate that our approach is able to detect humans in different postures (e. g. standing, walking, sitting) and with occlusions.

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IROS Conference 2012 Conference Paper

CoBots: Collaborative robots servicing multi-floor buildings

Manuela Veloso
Joydeep Biswas
Brian Coltin
Stephanie Rosenthal
Thomas Kollar
Çetin Meriçli
Mehdi Samadi
Susana Brandão

In this video we briefly illustrate the progress and contributions made with our mobile, indoor, service robots CoBots (Collaborative Robots), since their creation in 2009. Many researchers, present authors included, aim for autonomous mobile robots that robustly perform service tasks for humans in our indoor environments. The efforts towards this goal have been numerous and successful, and we build upon them. However, there are clearly many research challenges remaining until we can experience intelligent mobile robots that are fully functional and capable in our human environments.

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ICRA Conference 2012 Conference Paper

Efficient task execution and refinement through multi-resolution corrective demonstration

Çetin Meriçli
Manuela Veloso
H. Levent Akin

Computationally efficient task execution is very important for autonomous mobile robots endowed with limited on-board computational capabilities. Most robot control approaches assume fixed state and action representations, and use a single algorithm to map states to actions. However, not all instances of a given task require equally complex algorithms and equally detailed representations. The main motivation for this work is a desire to reduce the computational footprint of performing a task by allowing the robot to run simpler algorithms whenever possible, and resort to more complex algorithms only when needed. We contribute the Multi-Resolution Task Execution (MRTE) algorithm that utilizes human feedback to learn a mapping from a given state to an appropriate detail resolution consisting of a state and action representation, and an algorithm. We then present Model Plus Correction (M+C), an algorithm that complements an existing robot controller with corrective human feedback to further improve the task execution performance. Finally, we introduce Multi-Resolution Model Plus Correction (MRM+C) as a combination of MRTE and M+C. We provide formal definitions of MRTE, M+C, and MRM+C, showing how they relate to general robot control problem and Learning from Demonstration (LfD) methods. We present detailed experimental results demonstrating the effectiveness of proposed methods on a simulated goal-directed humanoid obstacle avoidance task.

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