What are the most common mistakes when using LiDAR in robotic sensing?

Calibration is the heartbeat of LiDAR in robotic sensing! Proper calibration aligns the sensor with reality, reducing errors. If misaligned, distorted, or noisy data can sabotage decisions. Following the manufacturer's guidance and using the right tools is key! Regular checks and updates ensure a reliable robotic perception. In my experience, precise calibration is the linchpin for accurate LiDAR sensing.

Common flaws can reduce the power of LiDAR in robotic sensing. Poor sensor selection, calibration, environmental constraints, and disregarding reflected surfaces are examples. Skipping calibration can distort data and impair robot navigation. Weather conditions like rain, snow, dust, and sunlight might impair LiDAR performance. Misinterpreting data or insufficient filtering might cause robot judgements to be wrong or overlook barriers. Not integrating LiDAR with other sensors or communication protocols can cause system integration challenges. Avoid these blunders to optimise your robotic LiDAR system for safe and efficient robot navigation and sensing.

Proper calibration of LiDAR sensors is crucial in robotic sensing. Calibration adjusts the sensor's parameters to reflect reality accurately and minimize errors. Incorrect calibration can result in distorted, misaligned, or noisy LiDAR data, leading to suboptimal results and unreliable decision-making. To prevent this, it's essential to adhere to the manufacturer's guidelines, employ suitable tools and methods for calibration, and routinely check and update the calibration as necessary.

Using a LiDAR sensor with inadequate resolution is a common error in robotic sensing. The resolution determines the sensor's ability to discern fine details in the environment. If the resolution is too low, vital information might be overlooked or distinct objects could be perceived as a single entity, complicating identification and classification. To avoid this, select a LiDAR sensor with a resolution that fits the application's needs and fine-tune the scan rate and angle as needed. Additionally, consider the balance between resolution and range, since typically, a higher resolution leads to a reduced range.

Most Lidar data will be affected by the surrounding .. light! ... reflection and metallic reflectors in the environment will definitely bias the data received by the Lidar. Two aspects have to be observed here, when it comes to measuring with precision: - an open angle beam (fov) will allow to eliminate blur area where the reflection occurs - Kalman filtering can help predict quite accurately the missing data within the blur area and cut the noise occurring in other area in the 'image'.

Real-time sensing for mobile robots is unique kind of game. One must design for the scenarios and time constants which are likely, while introducing safeguards for those which are unlikely but possible. For instance, if a robot with a 20m measuring range is moving at 1m/s in a scenario with humans, it should expect bodies moving in the scenario at about 5 to 8 m/s giving it 1 or 2 seconds to classify bodies on its way ahead. However, it should also include safeguards to halt and not run over any body appearing suddenly, regardless of identification or feature extraction, in case some cyclist cross or body drop from above. Software must be designed to compute in finite, known time, which adds constraints on the software and OS architecture.

The most common mistakes I’ve found with LiDAR are 1) insufficient knowledge of the environment, 2) wrong operation assumptions and 3) poor exception handling. Failing to account for dust, fog or dew; vegetation; sand, mud and slippery ground; excessive speed; damp indoors; dirty walls; weather variations; sun radiation (for 3D LiDAR); birds, dogs and toddlers; electromagnetic or GNSS interference; non-horizontal grounds and non-vertical walls; round corners, periodic scenes, has sent many engineers back to their design boards. Always expect the unexpected and design for reliability (= consistent performance at the standard operation conditions) and robustness (smooth performance degradation as operation conditions deviate from standard).