Zhengdong Zhang, Theia Henderson, Sertac Karaman, Vivienne Sze, "FSMI: Fast computation of Shannon Mutual Information for information-theoretic mapping," to appear in International Journal of Robotics Research (IJRR)AbstractPaperVideoPosterSlidesSoftware
Exploration tasks are embedded in many robotics applications, such as search and rescue and space exploration. Information-based exploration algorithms aim to find the most informative trajectories by maximizing an information- theoretic metric, such as the mutual information between the map and potential future measurements. Unfortunately, most existing information-based exploration algorithms are plagued by the computational difficulty of evaluating the Shannon mutual information metric. In this paper, we consider the fundamental problem of evaluating Shannon mutual information between the map and a range measurement. First, we consider 2D environments. We propose a novel algorithm, called the Fast Shannon Mutual Information (FSMI). The key insight behind the algorithm is that a certain integral can be computed analytically, leading to substantial computational savings. Second, we consider 3D environments, represented by efficient data structures, e.g., an OctoMap, such that the measurements are compressed by Run-Length Encoding (RLE). We propose a novel algorithm, called FSMI-RLE, that efficiently evaluates the Shannon mutual information when the measurements are compressed using RLE. For both the FSMI and the FSMI-RLE, we also propose variants that make different assumptions on the sensor noise distribution for the purpose of further computational savings. We evaluate the proposed algorithms in extensive experiments. In particular, we show that the proposed algorithms outperform existing algorithms that compute Shannon mutual information as well as other algorithms that compute the Cauchy-Schwarz Quadratic mutual information (CSQMI). In addition, we demonstrate the computation of Shannon mutual information on a 3D map for the first time.
Soumya Sudhakar, Sertac Karaman, Vivienne Sze, “Balancing Actuation and Computing Energy in Motion Planning,” IEEE International Conference on Robotics and Automation (ICRA), May 2020AbstractPaperVideoPosterSlidesSoftware
We study a novel class of motion planning prob- lems, inspired by emerging low-energy robotic vehicles, such as insect-size flyers, chip-size satellites, and high-endurance au- tonomous blimps, for which the energy consumed by computing hardware during planning a path can be as large as the energy consumed by actuation hardware during the execution of the same path. We propose a new algorithm, called Compute Energy Included Motion Planning (CEIMP). CEIMP operates similarly to any other anytime planning algorithm, except it stops when it estimates further computing will require more computing energy than potential savings in actuation energy. We show that CEIMP has the same asymptotic computational complexity as existing sampling-based motion planning algorithms, such as PRM*. We also show that CEIMP outperforms the average baseline of using maximum computing resources in realistic computational experiments involving 10 floor plans from MIT buildings. In one representative experiment, CEIMP outper- forms the average baseline 90.6% of the time when energy to compute one more second is equal to the energy to move one more meter, and 99.7% of the time when energy to compute one more second is equal to or greater than the energy to move 3 more meters.
Theia Henderson, Vivienne Sze, Sertac Karaman, “An Efficient and Continuous Approach to Information-Theoretic Exploration,” IEEE International Conference on Robotics and Automation (ICRA), May 2020AbstractPaperVideoPosterSlidesSoftware
Exploration of unknown environments is embed- ded and essential in many robotics applications. Traditional algorithms, that decide where to explore by computing the expected information gain of an incomplete map from future sensor measurements, are limited to very powerful computa- tional platforms. In this paper, we describe a novel approach for computing this expected information gain efficiently, as principally derived via mutual information. The key idea behind the proposed approach is a continuous occupancy map framework and the recursive structure it reveals. This structure makes it possible to compute the expected information gain of sensor measurements across an entire map much faster than computing each measurements’ expected gain independently. Specifically, for an occupancy map composed of |M| cells and a range sensor that emits |Θ| measurement beams, the algorithm (titled FCMI) computes the information gain corresponding to measurements made at each cell in O(|Θ||M|) steps. To the best of our knowledge, this complexity bound is better than all existing methods for computing information gain. In our experiments, we observe that this novel, continuous approach is two orders of magnitude faster than the state-of-the-art FSMI algorithm.
Peter Li*, Zhengdong Zhang*, Sertac Karaman, Vivienne Sze, "High-throughput Computation of Shannon Mutual Information on Chip," Robotics: Science and Systems (RSS), June 2019AbstractPaperVideoPosterSlidesSoftware
Exploration problems are fundamental to robotics, arising in various domains, ranging from search and rescue to space exploration. Many effective exploration algorithms rely on the computation of mutual information between the current map and potential future measurements in order to make planning decisions. Unfortunately, computing mutual information metrics is computationally challenging. In fact, a large fraction of the current literature focuses on approximation techniques to devise computationally-efficient algorithms. In this paper, we propose a novel computing hardware architecture to efficiently compute Shannon mutual information. The proposed architecture consists of multiple mutual information computation cores, each evaluating the mutual information between a single sensor beam and the occupancy grid map. The key challenge is to ensure that each core is supplied with data when requested, so that all cores are maximally utilized. Our key contribution consists of a novel memory architecture and data delivery method that ensures effective utilization of all mutual information computation cores. This architecture was optimized for 16 mutual information computation cores, and was implemented on an FPGA. We show that it computes the mutual information metric for an entire map of 20m × 20m at 0.1m resolution in near real time, at 2 frames per second, which is approximately two orders of magnitude faster, while consuming an order of magnitude less power, when compared to an equivalent implementation on a Xeon CPU.
Diana Wofk*, Fangchang Ma*, Tien-Ju Yang, Sertac Karaman, Vivienne Sze, "FastDepth: Fast Monocular Depth Estimation on Embedded Systems," International Conference on Robotics and Automation (ICRA), May 2019AbstractPaperVideoPosterSlidesSoftware
Depth sensing is a critical function for robotic tasks such as localization, mapping and obstacle detection. There has been a significant and growing interest in depth estimation from a single RGB image, due to the relatively low cost and size of monocular cameras. However, state-of-the-art single-view depth estimation algorithms are based on fairly complex deep neural networks that are too slow for real-time inference on an embedded platform, for instance, mounted on a micro aerial vehicle. In this paper, we address the problem of fast depth estimation on embedded systems. We propose an efficient and lightweight encoder-decoder network architecture and apply network pruning to further reduce computational complexity and latency. In particular, we focus on the design of a low-latency decoder. Our methodology demonstrates that it is possible to achieve similar accuracy as prior work on depth estimation, but at inference speeds that are an order of magnitude faster. Our proposed network, FastDepth, runs at 178 fps on an NVIDIA Jetson TX2 GPU and at 27 fps when using only the TX2 CPU, with active power consumption under 10 W. FastDepth achieves close to state-of-the-art accuracy on the NYU Depth v2 dataset. To the best of the authors’ knowledge, this paper demonstrates real-time monocular depth estimation using a deep neural network with the lowest latency and highest throughput on an embedded platform that can be carried by a micro aerial vehicle.
Zhengdong Zhang, Trevor Henderson, Vivienne Sze, Sertac Karaman, “FSMI: Fast computation of Shannon Mutual Information for information-theoretic mapping,” IEEE International Conference on Robotics and Automation (ICRA), May 2019AbstractPaperVideoPosterSlidesSoftware
Information-based mapping algorithms are crit- ical to robot exploration tasks in several applications rang- ing from disaster response to space exploration. Unfortu- nately, most existing information-based mapping algorithms are plagued by the computational difficulty of evaluating the Shan- non mutual information between potential future sensor mea- surements and the map. This has lead researchers to develop approximate methods, such as Cauchy-Schwarz Quadratic Mutual Information (CSQMI). In this paper, we propose a new algorithm, called Fast Shannon Mutual Information (FSMI), which is significantly faster than existing methods at computing the exact Shannon mutual information. The key insight behind FSMI is recognizing that the integral over the sensor beam can be evaluated analytically, removing an expensive numerical integration. In addition, we provide a number of approximation techniques for FSMI, which significantly improve computation time. Equipped with these approximation techniques, the FSMI algorithm is more than three orders of magnitude faster than the existing computation for Shannon mutual information; it also outperforms the CSQMI algorithm significantly, being roughly twice as fast, in our experiments.
Amr Suleiman, Zhengdong Zhang, Luca Carlone, Sertac Karaman, Vivienne Sze, "Navion: A 2mW Fully Integrated Real-Time Visual-Inertial Odometry Accelerator for Autonomous Navigation of Nano Drones," IEEE Journal of Solid State Circuits (JSSC), 54:4(1106-1119), April 2019AbstractPaperVideoPosterSlidesSoftware
This paper presents Navion, an energy-efficient accelerator for visual-inertial odometry (VIO) that enables au- tonomous navigation of miniaturized robots (e.g., nano drones), and virtual/augmented reality on portable devices. The chip uses inertial measurements and mono/stereo images to estimate the drone’s trajectory and a 3D map of the environment. This estimate is obtained by running a state-of-the-art VIO algorithm based on non-linear factor graph optimization, which requires large irregularly structured memories and heterogeneous compu- tation flow. To reduce the energy consumption and footprint, the entire VIO system is fully integrated on chip to eliminate costly off-chip processing and storage. This work uses compression and exploits both structured and unstructured sparsity to reduce on-chip memory size by 4.1×. Parallelism is used under tight area constraints to increase throughput by 43%. The chip is fabricated in 65nm CMOS, and can process 752×480 stereo images from EuRoC dataset in real-time at 20 frames per second (fps) consuming only an average power of 2mW. At its peak performance, Navion can process stereo images at up to 171 fps and inertial measurements at up to 52 kHz, while consuming an average of 24mW. The chip is configurable to maximize accuracy, throughput and energy-efficiency trade-offs and to adapt to different environments. To the best of our knowledge, this is the first fully-integrated VIO system in an ASIC.
Amr Suleiman, Zhengdong Zhang, Luca Carlone, Sertac Karaman, Vivienne Sze, "Navion: A Fully Integrated Energy-Efficient Visual-Inertial Odometry Accelerator for Autonomous Navigation of Nano Drones," IEEE Symposium on VLSI Circuits (VLSI-Circuits), June 2018AbstractPaperVideoPosterSlidesSoftware
This paper presents Navion, an energy-efficient accelerator for visual-inertial odometry (VIO) that enables autonomous navigation of miniaturized robots (e.g., nano drones), and virtual/augmented reality on portable devices. The chip uses inertial measurements and mono/stereo images to estimate the drone’s trajectory and a 3D map of the environment. This estimate is obtained by running a state-of-the-art algorithm based on non-linear factor graph optimization, which requires large irregularly structured memories and heterogeneous computation flow. To reduce the energy consumption and footprint, the entire VIO system is fully integrated on chip to eliminate costly off-chip processing and storage. This work uses compression and exploits both structured and unstructured sparsity to reduce on-chip memory size by 4.1x. Parallelism is used under tight area constraints to increase throughput by 43%. The chip is fabricated in 65nm CMOS, and can process 752x480 stereo images at up to 171 fps and inertial measurements at up to 52 kHz, while consuming an average of 24mW. The chip is configurable to maximize accuracy, throughput and energy-efficiency across different environments. To the best of our knowledge, this is the first fully integrated VIO system in an ASIC.
Zhengdond Zhang*, Amr Suleiman*, Luca Carlone, Vivienne Sze, Sertac Karaman, "Visual-Inertial Odometry on Chip: An Algorithm-and-Hardware Co-design Approach," Robotics: Science and Systems (RSS), July 2017AbstractPaperVideoPosterSlidesSoftware
Autonomous navigation of miniaturized robots (e.g., nano/pico aerial vehicles) is currently a grand challenge for robotics research, due to the need for processing a large amount of sensor data (e.g., camera frames) with limited on-board computational resources. In this paper we focus on the design of a visual-inertial odometry (VIO) system in which the robot estimates its ego-motion (and a landmark-based map) from on- board camera and IMU data. We argue that scaling down VIO to miniaturized platforms (without sacrificing performance) requires a paradigm shift in the design of perception algorithms, and we advocate a co-design approach in which algorithmic and hardware design choices are tightly coupled. Our contribution is four-fold. First, we discuss the VIO co-design problem, in which one tries to attain a desired resource-performance trade-off, by making suitable design choices (in terms of hardware, algorithms, implementation, and parameters). Second, we characterize the design space, by discussing how a relevant set of design choices affects the resource-performance trade-off in VIO. Third, we provide a systematic experiment-driven way to explore the design space, towards a design that meets the desired trade-off. Fourth, we demonstrate the result of the co-design process by providing a VIO implementation on specialized hardware and showing that such implementation has the same accuracy and speed of a desktop implementation, while requiring a fraction of the power.