In a recent article by Greg Nichols in ZDNet he explores the recent advances in machine vision and wonders is LiDAR going to be replaced by machine vision before it becomes mainstream? Here he interviews Rand Voorhies, CTO and co-founder at inVia Robotics.
GN: Can you give a little context on LiDAR adoption? Why has it become such a standardized sensing tool in autonomous mobility applications? What were the early hurdles to machine vision that led developers to LiDAR?
Rand Voorhies: Machine vision has been used to guide robots since before LiDAR existed. LiDAR started gaining significant popularity in the early 2000s due to some groundbreaking academic research from Sebastian Thrun, Daphne Koller, Michael Montemerlo, Ben Wegbreit, and others that made processing data from these sensors feasible. That research and experience led to the dominance of the LiDAR-based Stanley autonomous vehicle in the DARPA Grand Challenge (led by Thrun), as well as to the founding of Velodyne (by David Hall, another Grand Challenge participant), which produces what many now consider to be the de-facto autonomous car sensor. The Challenge showed that LiDAR was finally a viable technology for fast-moving robots to navigate through unknown, cluttered environments at high speeds. Since then, there has been a huge increase in academic interest in improving algorithms for processing LiDAR sensor data, and there have been hundreds of papers published and PhDs minted on the topic. As a result, graduates have been pouring into the commercial space with heaps of academic LiDAR experience under their belt, ready to put theory to practice.
In many cases, LiDAR has proven to be very much the right tool for the job. A dense 3D point cloud has long been the dream of roboticists and can make obstacle avoidance and pathfinding significantly easier, particularly in unknown dynamic environments. However, in some contexts, LiDAR is simply not the right tool for the job and can add unneeded complexity and expense to an otherwise simple solution. Determining when LiDAR is right and when it’s not is key to building robotic solutions that don’t just work — they also provide positive ROI to the customer.
At the same time, machine vision has advanced as well. One of the early hurdles in machine vision can be understood with a simple question: “Am I looking at a large object that’s far away or a tiny object that’s up-close”? With traditional 2D vision, there was simply no way to differentiate. Even our brains can be fooled, as seen in funhouse perspective illusions. Modern approaches to machine vision use a wide range of approaches to overcome this, including:
Estimating the distance of an object by understanding the larger context of the scene, e.g., I know my camera is 2m off the ground, and I understand that car’s tires are 1000 pixels along the street, so it must be 25m away.
Building a 3D understanding of the scene by using two or more overlapping cameras (i.e., stereo vision).
Building a 3D understanding of the scene by “feeling” how the camera has moved, e.g., with an IMU (inertial measurement unit – sort of like a robot’s inner ear) and correlating those movements with the changing images from the camera.
Our own brains use all three of these techniques in concert to give us a rich understanding of the world around us that goes beyond simply building a 3D model.
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