More Like this

1. Active Safety Envelopes using Light Curtains with Probabilistic Guarantees

   https://doi.org/10.15607/RSS.2021.XVII.045  

   Ancha, Siddharth; Pathak, Gaurav; Narasimhan, Srinivasa; Held, David (July 2021, Robotics: Science and Systems)

   null (Ed.)

   To safely navigate unknown environments; robots must accurately perceive dynamic obstacles. Instead of directly measuring the scene depth with a LiDAR sensor; we explore the use of a much cheaper and higher resolution sensor: programmable light curtains. Light curtains are controllable depth sensors that sense only along a surface that a user selects. We use light curtains to estimate the safety envelope of a scene: a hypothetical surface that separates the robot from all obstacles. We show that generating light curtains that sense random locations (from a particular distribution) can quickly discover the safety envelope for scenes with unknown objects. Importantly; we produce theoretical safety guarantees on the probability of detecting an obstacle using random curtains. We combine random curtains with a machine learning based model that forecasts and tracks the motion of the safety envelope efficiently. Our method accurately estimates safety envelopes while providing probabilistic safety guarantees that can be used to certify the efficacy of a robot perception system to detect and avoid dynamic obstacles. We evaluate our approach in a simulated urban driving environment and a real-world environment with moving pedestrians using a light curtain device and show that we can estimate safety envelopes efficiently and effectively. 

   more » « less
2. Active Perception using Light Curtains for Autonomous Driving

   https://doi.org/10.1007/978-3-030-58558-7_44  

   Ancha, Siddharth; Raaj, Yaadhav; Hu, Peiyun; Narasimhan, Srinivasa; Held, David (January 2020, European Conference on Computer Vision)

   null (Ed.)

   Most real-world 3D sensors such as LiDARs perform fixed scans of the entire environment, while being decoupled from the recognition system that processes the sensor data. In this work, we propose a method for 3D object recognition using light curtains, a resource-efficient controllable sensor that measures depth at user-specified locations in the environment. Crucially, we propose using prediction uncertainty of a deep learning based 3D point cloud detector to guide active perception. Given a neural network's uncertainty, we derive an optimization objective to place light curtains using the principle of maximizing information gain. Then, we develop a novel and efficient optimization algorithm to maximize this objective by encoding the physical constraints of the device into a constraint graph and optimizing with dynamic programming. We show how a 3D detector can be trained to detect objects in a scene by sequentially placing uncertainty-guided light curtains to successively improve detection accuracy. 

   more » « less
3. Exploiting & Refining Depth Distributions with Triangulation Light Curtains

   Yaadhav, Raaj; Ancha, Siddharth; Tamburo, Robert; Held, David; Narasimhan, Srinivasa G. (June 2021, IEEE Conference on Computer Vision and Pattern Recognition)

   Active sensing through the use of Adaptive Depth Sensors is a nascent field, with potential in areas such as Advanced driver-assistance systems (ADAS). They do however require dynamically driving a laser / light-source to a specific location to capture information, with one such class of sensor being the Triangulation Light Curtains (LC). In this work, we introduce a novel approach that exploits prior depth distributions from RGB cameras to drive a Light Curtain’s laser line to regions of uncertainty to get new measurements. These measurements are utilized such that depth uncertainty is reduced and errors get corrected recursively. We show real-world experiments that validate our approach in outdoor and driving settings, and demonstrate qualitative and quantitative improvements in depth RMSE when RGB cameras are used in tandem with a Light Curtain. 

   more » « less
4. Photometric Monitoring of Electronic Cigarette Puff Topography

   https://doi.org/10.3390/s23198220  

   Kolaczyk, Keith; Jiang, Hao (October 2023, Sensors)

   To study and monitor the adverse health consequences of using electronic cigarettes, a user’s puff topography, which are quantification parameters of the user’s vaping habits, plays a central role. In this work, we introduce a topography sensor to measure the mass of total particulate matter generated in every puff and to estimate the nicotine yield. The sensor is compact and low-cost, and is integrated into the electronic cigarette device to promptly and conveniently monitor the user’s daily puff topography. The topography sensor is comprised of a photometric sensor and a pressure sensor. The photometric sensor measures the mass concentration of the aerosol, based on scattering of near-infrared light from airborne particles, while the pressure sensor measures the flow rate. The topography sensor was tested under various conditions including a wide range of atomizer power, puff duration, and inhalation pressure. The sensor’s accuracy was validated by comparing the sensor’s readings with reference measurements, and the results matched closely with the trends reported by existing studies on electronic cigarettes. An example application for tracking a user’s puff topography was also demonstrated. Our topography sensor holds great promise in mitigating the health risks of vaping, and in promoting quality control of electronic cigarette products. 

   more » « less
5. Energy-Efficient Adaptive 3D Sensing

   Tilmon, Brevin; Sun, Zhanghao; Koppal, Sanjeev J.; Wu, Yicheng; Evangelidis, Georgios; Zahreddine, Ramzi; Krishnan, Gurunandan; Ma, Sizhuo; Wang, Jian (June 2023, IEEE Conference on Computer Vision and Pattern Recognition)

   Active depth sensing achieves robust depth estimation but is usually limited by the sensing range. Naively increas- ing the optical power can improve sensing range but in- duces eye-safety concerns for many applications, including autonomous robots and augmented reality. In this paper, we propose an adaptive active depth sensor that jointly opti- mizes range, power consumption, and eye-safety. The main observation is that we need not project light patterns to the entire scene but only to small regions of interest where depth is necessary for the application and passive stereo depth es- timation fails. We theoretically compare this adaptive sens- ing scheme with other sensing strategies, such as full-frame projection, line scanning, and point scanning. We show that, to achieve the same maximum sensing distance, the proposed method consumes the least power while having the shortest (best) eye-safety distance. We implement this adaptive sensing scheme with two hardware prototypes, one with a phase-only spatial light modulator (SLM) and the other with a micro-electro-mechanical (MEMS) mirror and diffractive optical elements (DOE). Experimental results validate the advantage of our method and demonstrate its capability of acquiring higher quality geometry adaptively. Please see our project website for video results and code: https://btilmon.github.io/e3d.html 

   more » « less