Paper Info
Title
An 879GOPS 243mW 80fps VGA Fully Visual CNN-SLAM Processor for Wide-Range Autonomous Exploration
Abstract
Simultaneous localization and mapping (SLAM) estimates an agent’s trajectory for all six degrees of freedom (6 DoF) and constructs a 3D map of an unknown surrounding. It is a fundamental kernel that enables head-mounted augmented/virtual reality devices and autonomous navigation of micro aerial vehicles. A noticeable recent trend in visual SLAM is to apply computation- and memory-intensive convolutional neural networks (CNNs) that outperform traditional hand-designed feature-based methods [1]. For each video frame, CNN-extracted features are matched with stored keypoints to estimate the agent’s 6-DoF pose by solving a perspective-n-points (PnP) non-linear optimization problem (Fig. 7.3.1, left). The agent’s long-term trajectory over multiple frames is refined by a bundle adjustment process (BA, Fig. 7.3.1 right), which involves a large-scale ($\sim$120 variables) non-linear optimization. Visual SLAM requires massive computation ($\gt250$ GOP/s) in the CNN-based feature extraction and matching, as well as data-dependent dynamic memory access and control flow with high-precision operations, creating significant low-power design challenges. Software implementations are impractical, resulting in 0.2s runtime with a $\sim$3 GHz CPU + GPU system with $\gt100$ MB memory footprint and $\gt100$ W power consumption. Prior ASICs have implemented either an incomplete SLAM system [2, 3] that lacks estimation of ego-motion or employed a simplified (non-CNN) feature extraction and tracking [2, 4, 5] that limits SLAM quality and range. A recent ASIC [5] augments visual SLAM with an off-chip high-precision inertial measurement unit (IMU), simplifying the computational complexity, but incurring additional power and cost overhead.
Year
DOI
Venue
2019
10.1109/ISSCC.2019.8662397
2019 IEEE International Solid- State Circuits Conference - (ISSCC)
Field
DocType
Volume
Computer vision,Bundle adjustment,Computer science,Convolutional neural network,Feature extraction,Electronic engineering,Inertial measurement unit,Artificial intelligence,Simultaneous localization and mapping,Memory footprint,Optimization problem,Computational complexity theory
Conference
62
ISSN
ISBN
Citations 
0193-6530
978-1-5386-8531-0
5
PageRank 
References 
Authors
0.50
0
7
Name
Order
Citations
PageRank
Ziyun Li1326.62
Yu Chen251749.61
Luyao Gong361.52
Lu Liu41501170.70
Dennis Sylvester55295535.53
David Blaauw68916823.47
Hun-Seok Kim7579.07