I want to use an already implemented SLAM algorithm for mapping my college campus.
I have found some algorithms on OpenSLAM.org and some other independent ones such as LSD-SLAM and Hector SLAM, which show some promises but they have limitation such as they use LIDAR, or don't extend to large dataset etc.
SLAM has been an active topic for many years and some groups have also mapped an entire town. Can someone point me to such efficient algorithm?
My requirements are:
It must use RGB camera/cameras.
Preferably produce (somewhat) dense map of area.
It should be able to map large area (I have seen some algo which can only map up to a desk table or room, but they usually lose track if there is jerk in camera motion (Observed in LSD SLAM) or take very few landmarks which is only useful for study purposes).
Preferably a ROS implementation.
Related
I want to create high fidelity meshes of urban street scenes using pointcloud data. The data consists of pointclouds using a HDL64-e and the scenes are very similar to the one in the Kitti Dataset.
Currently im only able to use the 'raw' point clouds and odometry of the car. Previous works already implemented the LeGO-LOAM algorithm to create a monolithic map and better odometry estimates.
Available Data:
Point Clouds with 10Hz timings
Odometry estimates with higher frequencies (LOAM Output)
Monolithic map of the scene (LOAM Output) (~1.500.000 Points)
I already did some research and came to the conclusion, that I can either
use the monolithic map with algorithms like Poisson Reconstruction, Advancing Front, etc... (using CGAL)
go the robotics way and use some packages like Voxgraph (which uses Marching Cubes internally)
As we might want to integrate image data at a later step the second option would be preferred.
Questions:
Is there a State-of-the-Art way to go?
Is it possible to get a mesh that can preserve small features like curbs and sign posts? (I know there might be a feasable limit on how fine the mesh can be)
I am very interested in some feedback and a discourse on how to tackle this problem 'the right way'.
Thank you for your suggestions/answers in advance!
i am doing final year project as lane tracking using a camera. the most challenging task now is how i can measure distance between the camera (the car that carries it actually) and the lane.
While the lane is easily recognized (Hough line transform) but i found no way to measure distance to it.
given the fact that there is a way to measure distance to object in front of camera based on Pixel width of the object, but it does not work here be because the nearest point of the line, is blind in the camera.
What you want is to directly infer the depth map with a monocular camera.
You can refer my answer here
https://stackoverflow.com/a/64687551/11530294
Usually, we need a photometric measurement from a different position in the world to form a geometric understanding of the world(a.k.a depth map). For a single image, it is not possible to measure the geometric, but it is possible to infer depth from prior understanding.
One way for a single image to work is to use a deep learning-based method to direct infer depth. Usually, the deep learning-based approaches are all based on python, so if you only familiar with python, then this is the approach that you should go for. If the image is small enough, i think it is possible for realtime performance. There are many of this kind of work using CAFFE, TF, TORCH etc. you can search on git hub for more option. The one I posted here is what i used recently
reference:
Godard, Clément, et al. "Digging into self-supervised monocular depth estimation." Proceedings of the IEEE international conference on computer vision. 2019.
Source code: https://github.com/nianticlabs/monodepth2
The other way is to use a large FOV video for a single camera-based SLAM. This one has various constraints such as need good features, large FOV, slow motion, etc. You can find many of this work such as DTAM, LSDSLAM, DSO, etc. There are a couple of other packages from HKUST or ETH that does the mapping given the position(e.g if you have GPS/compass), some of the famous names are REMODE+SVO open_quadtree_mapping etc.
One typical example for a single camera-based SLAM would be LSDSLAM. It is a realtime SLAM.
This one is implemented based on ROS-C++, I remember they do publish the depth image. And you can write a python node to subscribe to the depth directly or the global optimized point cloud and project it into a depth map of any view angle.
reference: Engel, Jakob, Thomas Schöps, and Daniel Cremers. "LSD-SLAM: Large-scale direct monocular SLAM." European conference on computer vision. Springer, Cham, 2014.
source code: https://github.com/tum-vision/lsd_slam
What is the exact definition of spatial and temporal? I saw in many places people use these two terms, e.g., spatial vector, temporal vector, temporal factor, spatial location.
I was searching in StackOverflow, and found this one- what's the difference between spatial and temporal characterization in terms of image processing?
What I understood so far is that the term spatial is related to space and the term temporal is related to time. Still, it is quite abstract to me. Again, I am also not sure about the uses of these two. So, as same as the person asked in the above link, I want to ask the same question- What do these two terms mean and why do we care about these two?
Spatial data have to do with location-aware information, in other words, data that have coordinates (x, y). A typical example of spatial data is latitude and longitude in geographic datasets. Spatial analyses are the techniques involved in analyzing spatial data. This is a significant component of GIS (Geographic Information Systems/Science)
Temporal data is time-series data. In other words, this is data that is collected as time progresses. Temporal analysis is also known as Time-Series analysis. These are the techniques for analyzing data units that change with time.
I hope this makes these concepts less abstract and more concrete.
Adding to Ekaba's answer, spatial data doesn't necessarily need to be two dimensional either. I'm going to take an example from a medical domain which would have both spatial and temporal elements of data.
If you consider magnetic resonance imaging, it is essentially a 3D Volumetric view of an organ (let's say brain for clarity). So if you are to analyse a traditional MRI, it would be spatial analysis and you'll have 3 dimensions as it is 3D. There's another MRI modality called DCE-MRI which is essentially a sequence of MRI volumes captured over time. Now this is a typical example of a temporal sequence. Let's say DCE-MRI sequence has 40 MRI volumes captured 20s apart from each. If you just consider one sequence out of these 40 and analyse that, you'll be analyzing it spatially whereas if you consider all 40 (or a subset) of these volumes at the same time, you are analyzing it spatially as well as temporally.
Hope that clarifies things.
Another similar medical example is ultrasound imaging of a beating heart (2D Echocardiography) where the ultrasound image shows opening and closing movement of heart valves in real-time and volumetric movement of heart chambers. With high temporal resolution (# 30 frames per second) it is easy to follow the valves opening and closing accurately. With high spatial resolution it is also easy to differentiate boarders of the heart chambers to provide accurate volumetric blood flow data.
I'm looking for a way to detect humans in a picture. For instance, regarding the picture below, I'd like to coarsely determine how many people are in the scene. I must be able to detect both standing and sitting people. I do not mind not detecting people located behind a physical object (such as the glass in the bus picture).
AFAIK, such a problem can rather easily be solved by training deep neural networks. However, my coworkers would like me to also implement a detection technique based on general image processing techniques. I've spent several days looking for techniques designed by researchers but I couldn't find anything else than saliency-based techniques (which may be fine, but I'd like to test several techniques based on old-fashioned image processing).
I'd like to mention that I'm not new to the topic of image segmentation & I used to segment aortas in medical scans. However, this task was easier IMHO since scanners have similar features: in this use-case (human detection in a bus, for instance), the pictures will have very different characteristics (e.g. image contrast can strongly vary, whether it's been taken during the day or at night).
Long story short, I'd like to know if there's some segmentation technique for human detection for which it'd be interesting giving a shot, given the fact that the images features vary a lot?
Is deep learning the only way to detect humans in a picture?
No. Is it the best way we know? Depends on your conditions.
The simplest way of detection is to generate lots of random bounding boxes and then solving the classification problem of the crop. Here is some pythonic pseudo-code:
def detect_people(image):
"""
Find all people in image.
Parameters
----------
image : image object
Returns
-------
people : list of axis-aligned bounding boxes (aabb)
Each bounding box contains a person
"""
people = []
for aabb in generate_random_aabb(image):
crop = crop_image(image, aabb)
if is_person(crop):
people.append(crop)
return people
In this case is_person can be any classifier, e.g. boosted decision stumps as used in the Viola–Jones object detection framework. Speaking of which: That would likely be the way to go without DL, but is much more complicated to explain.
Object Detection vs Segmentation
Your question mixes both. Object detection gives you bounding boxes (coarse) for instances. Semantic segmentation labels all pixels by classes, but does not distinguish different instances of the same class (e.g. different people). Instance segmentation is like object detection, but is fine-grained and aims for pixel-exact results.
If you are interested in segemantation, I can recommend my paper: A Survey of Semantic Segmentation
I am right now in a serious problem, I need to compare images of flowers (carnations) using a genetic algorithm, the program must determine which variety does the flower belongs to (until now I am using 15 different varieties), the thing is I am having difficulties constructing the chromosome, right know I am only analysing the HSV of each image, then a take every channel and calculate the mean for each (n=255), after that I calculate the correlation between HS, HV and SV, I expected that the mean would be enough to locate any new flower next to the clusters of flowers of the variety it belongs (by the way, I have a database of all the flowers used for training purpose) by calculating the distance between the mean of the flower and the centroids of each cluster, and probably using the correlations for adjustment, but that distance is usually way smaller to a different variety than the one it must be. Is there a way to classify this flowers using ONLY colours (I've read of applications that uses the texture, but that's way out of my league), especially using a genetic algorithm (I know Neural Networks are more appropriate to this kind of analysis but that's what the teacher asked)?. Thank you very much. By the way I am working on OpenCV, don’t know if it's relevant. PS: Excuse my English if any mistakes were done, not my native language.