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Fragmentation of results removing instead of increasing ambiguity doesn't pass the smell check, neither isn't it not something exactly against the leading FSD e

by maxlin 1y ago

Fragmentation of results removing instead of increasing ambiguity doesn't pass the smell check, neither isn't it not something exactly against the leading FSD engineers have commented on the issue while they were removing other non-uniform sensors, even disabling existing ones entirely in cars. Trying to claim some entirely different stack in some third party LIDAR tower's own processing is somehow "beneficial" sounds like a project manager who thinks adding engineers equates to linearly faster progress. Just no. On a slight tangent though, I recommend reading about Tesla's vertical integration. It's not something any other company has managed to get implemented so deep in automotive, which makes it quite incomparable in some aspects where others can't adapt even if they wanted to.

Joel_Mckay 1y ago
Admittedly, I only have limited out-of-date knowledge from integrating iBeo, hokuyo, and SiCK lidar models (way better models out now.) Yet in general, the SLAM algorithms tended to use optical Odometry, pose recovery, and point-cloud data. Then extrapolated the projected surface using camera data within a GPU accelerated OpenCV layer. It does require a proper global shutter machine vision camera (high fps / no smear), but does work fairly well under ideal conditions. The winners of the Darpa grand challenge documented the methods in detail, and several OpenCV books cite the work as a student project. Let me know if you have trouble finding the projects. =3
- maxlin 1y ago
  gluing together third party stacks like there's no tomorrow doesn't make you better evaluating how a vision-only ML model with limited, yet uniquely hardware-accelerated, one-task actual real life, real-time ML model running right now would integrated with foreign data, especially as the actual relevant engineers have directly talked against such things. It might make you headstrong in believing against something that'd be easier to see the core sensibility of if you weren't so invested in just a specific corner/angle though. I've avoided working a work project involving LIDAR scanning before, even back then the hellishness of the hardware was a large factor. I wouldn't mind playing around with a Jetson Nano though.
  - Joel_Mckay 1y ago
    >gluing together third party stacks like there's no tomorrow That is why I don't really like ROS. lol =3 >doesn't make you better evaluating how a vision-only ML model In general, the monocular SLAM algorithms rely on salient feature extraction, and several calibrated assumptions about the camera platform. How you interpret that output is another set of issues, as the power budget is going to take the hit. For machine vision, I'd skip the proprietary Jetson Nano... and get a cheap gaming "parts" laptop with a broken LCD and several USB ports (RTX4090 or RTX4080 is a trophy.) No one wants to fork over $30k for an outdoor lidar, but using only cameras is a fools errand. The best platforms I've seen commercially use camera + lidar + radar. For student projects, one can get small radars and TOF sensors for under $20 off sparkfun (similar to the one in iPhone Pro 11/12/13). We live in the future... =3

by maxlin 1y ago

Joel_Mckay 1y ago

Admittedly, I only have limited out-of-date knowledge from integrating iBeo, hokuyo, and SiCK lidar models (way better models out now.) Yet in general, the SLAM algorithms tended to use optical Odometry, pose recovery, and point-cloud data. Then extrapolated the projected surface using camera data within a GPU accelerated OpenCV layer. It does require a proper global shutter machine vision camera (high fps / no smear), but does work fairly well under ideal conditions. The winners of the Darpa grand challenge documented the methods in detail, and several OpenCV books cite the work as a student project. Let me know if you have trouble finding the projects. =3

maxlin 1y ago
gluing together third party stacks like there's no tomorrow doesn't make you better evaluating how a vision-only ML model with limited, yet uniquely hardware-accelerated, one-task actual real life, real-time ML model running right now would integrated with foreign data, especially as the actual relevant engineers have directly talked against such things. It might make you headstrong in believing against something that'd be easier to see the core sensibility of if you weren't so invested in just a specific corner/angle though. I've avoided working a work project involving LIDAR scanning before, even back then the hellishness of the hardware was a large factor. I wouldn't mind playing around with a Jetson Nano though.
- Joel_Mckay 1y ago
  >gluing together third party stacks like there's no tomorrow That is why I don't really like ROS. lol =3 >doesn't make you better evaluating how a vision-only ML model In general, the monocular SLAM algorithms rely on salient feature extraction, and several calibrated assumptions about the camera platform. How you interpret that output is another set of issues, as the power budget is going to take the hit. For machine vision, I'd skip the proprietary Jetson Nano... and get a cheap gaming "parts" laptop with a broken LCD and several USB ports (RTX4090 or RTX4080 is a trophy.) No one wants to fork over $30k for an outdoor lidar, but using only cameras is a fools errand. The best platforms I've seen commercially use camera + lidar + radar. For student projects, one can get small radars and TOF sensors for under $20 off sparkfun (similar to the one in iPhone Pro 11/12/13). We live in the future... =3